Challenges in network science: Applications to infrastructures, climate, social systems and economics

Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected. Graphical abstrac

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected

On the Four Types of Weight Functions for Spatial Contiguity Matrix

This is a "spatial autocorrelation analysis" of spatial autocorrelation. I use the 1-dimension spatial autocorrelation function (ACF) and partial autocorrelation function (PACF) to analyze four kinds of weight function in common use for the 2-dimensional spatial autocorrelation model. The aim of this study is at how to select a proper weight function to construct a spatial contiguity matrix for spatial analysis. The scopes of application of different weight functions are defined in terms of the characters of their ACFs and PACFs.Comment: 8 pages, 5 figures, 2 table

Modelling Hierarchy and Specialization of a System of Cities from an Evolutionary Perspective on Firms' Interactions

Despite their great diversity, most systems of cities show remarkably similar patterns when comparing the size distribution and the economic specialization of their constitutive cities. The universality of these patterns sparked the interest of geographers, economists and physicists. However, until now, no economic model has relied on a micro-based and evolutionary approach to reproduce these regularities. In this chapter, we intend to fill this gap by proposing a model where the micro dynamics of localized firms generate the two macro regularities of size distribution and economic specialization. The model is based on boundedly rational firms’ competition and path dependent innovation. We discuss the possible emergence of macro properties from these micro behaviors of firms

Planning and complexity: Engaging with temporal dynamics, uncertainty and complex adaptive systems

The nature of complex systems as a transdisciplinary collection of concepts from physics and economics to sociology and ecology provides an evolving field of inquiry (Laszlo and Krippner, 1998) for urban planning and urban design. As a result, planning theory has assimilated multiple concepts from the complexity sciences over the past decades. The seemingly chaotic or non-linear urban phenomena resulting from the combination of hard and soft systems (Checkland, 1989) or physical and environmental aspects of the city with human intervention, motivation and perception have been of particular interest in the context of increasing criticism of top-down approaches. Processes such as self- organisation, temporal dynamics and transition, previously ignored or assumed problematic within equilibrium-centred conceptualisations or mechanistic theories, have found their way back into planning through complexity theories of cities (CTC) (Allen, 1997; Batty, 2007; de Roo and Silva, 2010; Marshall, 2012; Portugali, 2011b). While there is an overlap with Structuralist-Marxist and humanistic perspectives (Portugali, 2011c) and a continuity from an older science of cities (Batty, 2013), it is interesting to observe the engagement with bottom-up phenomena, structural and functional co-evolution and resultant adaptable and self-organisational systems within complexity planning. It has taken time for planning to adopt complexity thinking beyond metaphor or common usage of the term, but we now appear to be at a tipping point where complexity planning is exploring methods of engagement and cognition, rather than the question of whether cities are complex

HOUSE 1 Protostructure: Enhancement of Spatial Imagination and Craftsmanship Between the Digital and the Analogical

Conceived around the concept of protostructure, HOUSE 1 deploys a strategy to answer a daring but simple question: How could we design a house between almost 300 people? The unique pedagogical framework of ALICE, first year Architectural Design course, proposes the integration of a series of full scale physical wooden constructs, enacting collaborative thinking and drawing on collective spatial knowledge. The protostructure constitutes at once both a material and immaterial open source support for the individual and collective interventions by the students. Its material dimension as a physical construction is invested and complemented by the immateriality of the guiding scheme. In this article, we review the steps in the development of the theoretical model and physical implementation of HOUSE 1 and discuss its relevance with regards to the relation between analogical and digital modes of engagement, pedagogical frameworks and spatial cognitive strategies. This implementation of the protostructure shows its potential as a tool to approach wood design, through a combination of digital and analogical processes, enhancing the deployment of spatial cognitive strategies with the use of wood as a material through and with which to think about space

GIS and spatial data analysis: Converging perspectives

We take as our starting point the state of geographic information systems (GIS) and spatial data analysis 50 years ago when regional science emerged as a new field of enquiry. In the late 1950s and 1960s advances in computing technology were making possible forms of automated cartography that in due course would lead to th

Land use interpretation for cellular automata models with socioeconomic heterogeneity

Cellular automata models for simulation of urban development usually lack the social heterogeneity that is typical of urban environments. In order to handle this shortcoming, this paper proposes the use of supervised clustering analysis to provide socioeconomic intra-urban land use classification at different levels to be applied to cellular automata models. An empirical test in a highly diverse context in the Greater Metropolitan Area of Belo Horizonte (RMBH) in Brazil is provided. The results show that a reliable division into different socioeconomic land-use classes at large scale enable detailed urban dynamic analysis. Furthermore, the results also allow the quantification of the proportion of urban space occupation for different levels of income; (2) and their pattern in relation to the city centre