Simone Caschili

Accessibility and rurality indicators for regional development

Abstract The development of a region is affected, inter alia, by concepts linked to the ability to displace and reach other locations (accessibility) efficiently and to lagging economic conditions connected to contemporary countryside activities (rurality). These topics and their relationships have attracted the interest of scholars who have scrutinized the implications of accessibility and rurality for policy making and planning. The aim of this paper is to contribute to the theoretical modeling of accessibility and rurality and to develop an empirical study of their spatial patterns, with reference to the municipalities of the region of Sardinia, Italy. We study accessibility through an indicator constructed using a doubly constrained spatial interaction model and propose the Composite Index of Rurality that aims to evaluate rurality in a regional setting employing multivariate analysis. We investigate the spatial dependence of these indicators through general and local spatial autocorrelation analysis to verify the hypothesis that scarcely accessible spatial units are classifiable as rural areas. The results show that, for the case study of Sardinia, this hypothesis is not always true, as some urban areas are not always highly accessible.

Time Evolution of Complex Networks: Commuting Systems in Insular Italy

The aim of this paper is to study the dynamics of the commuting system of two insular regions of Italy, Sardinia and Sicily, inspected as complex networks. The authors refer to a 20-year time period and take into account three census data sets about the work and study-driven inter-municipal origin-destination movements of residential inhabitants in 1981, 1991 and 2001. Since it is likely that the number of municipalities (in this case, the vertices of the system) does not display sharp variations, the authors direct the study to the variation of the properties emerging through both a topological and a weighted network representation of commuting in the time periods indicated.

Unveiling co-operative networks and ‘hidden families’ in the container port industry

Over the last few years, the progressive scarcity of land for greenfield projects in ports and the enormous financial resources required to realize new terminal facilities have induced international terminal operators (ITOs) to enter in various types of agreements. In particular, ITOs have growingly resorted to equity joint-ventures (EJVs) to develop new infrastructures and share project risks. This manuscript introduces a multi-layer conceptual framework and investigates the extent of EJVs through a network analysis of inter-firm communities across various nations. The study has been performed on a data set that includes all port facilities operated in 2010 by ITOs. Indeed, 427 terminal facilities and 250 container port operators, that is 40 ITOs and 210 local firms, have been examined. Results show the different co-operative attitudes of ITOs and demonstrate that firms co-operate in a selective way, disclosing emerging patterns and hidden networks of relationships.

Dealing with uncertainty and volatility in the port industry network: social and instrumental antecedents of “clique” survival

The recent proliferation of inter-firm collaborative linkages within the container port industry is progressively shaping a complex architecture of voluntary ties among terminal operators, i.e. an inter-organizational network. Within the overall industry network, some stable groups of densely interconnected firms emerge suggesting the existence of “cliques” where firms repeatedly cooperate with each other and develop strong and durable ties. The growing pressure exerted by economic uncertainty and market volatility on clique stability stimulates a fruitful academic debate on clique survival and its determinants. The present contribution investigates social and instrumental antecedents of clique survival, focusing on equity joint ventures engaged by terminal operators in a 10-year time frame (2002–2011). For this purpose, this study introduces a longitudinal network analysis of inter-firm relationships. Research hypotheses are tested performing binomial logistic regression analysis. In line with network theory assumptions, the outcomes corroborate the explanatory role of a number of social (i.e., cultural similarity and organizational mismatch) and instrumental (i.e., the presence of core firms within the clique and clique equality) antecedents in the survival of cliques. The results, indeed, bring valuable implications for both academics and practitioners.

Measuring the scope of inter-firm agreements in the container shipping industry: an empirical assessment

In container shipping industry inter-firm agreements are becoming progressively popular as ship-owners share their slot capacity with commercial partners in order to have fully loaded container ships and reduce financial risk. This manuscript focuses on the cooperative agreements among shipping firms, i.e., vessel sharing and slot charter agreements within consortia and strategic alliances. Through a quantitative approach based on network and OLS regression analysis, we scrutinise the propensity to cooperate, the geographic extent and ‘leveraging effect’ generated by this commercial practise on the container-shipping industry. Results show that carriers, usually regarded as independent, are instead fairly cooperative, especially when involved in trade lanes originating from the Far East. Finally, we show that carriers increase their commercial objectives by leveraging the operated fleet capacity. We conclude with some implications for managers and practitioners as well as a discussion on limitations and future extensions of this study.

Complex Networks Analysis of Commuting

The emerging new science of networks is providing an elegant paradigm for the characterization of the broad area of complex systems. New research perspectives have been opened in the study of many real phenomena and processes, and recently fields like urban, regional, and environmental sciences have gained new insights from the tools provided by network science. The complex networks analysis (CNA) becomes a useful framework in these fields to disentangle problems of a complex and unpredictable nature.

Recent Developments of Complex Network Analysis in Spatial Planning

In the last years, we acknowledge a great scientific interest on complex network analysis, a method able to characterise systems with very large numbers of entities (the nodes or vertices) interlaced by a series of connections/relationships (the links or edges). The objects of analyses as such are biological (predator-pray); information (internet); social (actor-in the same movie); transportation (railway and road networks) systems. While in general a network is an abstract (topo) logical object, spatial networks belong to an important class of systems that includes nodes and edges with a clear reference to space. Recently the interest of scientists has focused on methods able to define and investigate on communities emerging from the structure of a network. In this respect the spatial factor can emerge both as the result of the topological community structure that maps back onto geography in the form of sensible spatial regions, or just as spatial clusterisation of nodes in principle embedded in space. In this essay, the authors aim at presenting a state of the art summary of the last advances in the field of network community detection methodologies with a detailed view to the case of spatial networks. Secondly, the paper will report on a case study concerning a major issue for policy makers and planners: the delimitation of sub-regional domains showing a sufficient level of homogeneity with respect to some specific territorial features. We compare some intermediate body partitions of the island of Sardinia (Italy) with the patterns of the communities of workers and students, by applying grouping methodologies based on the characterisation of the Sardinian commuters’ system as a complex weighted network.

The Implementation of the Uganda Agent Based Model

We focus on the seven districts mentioned in Chap. 9, located in the Central and Eastern regions of Uganda—Luwero, Mpigi, Masaka, Iganga, Mitiyana, Kamuli, and Mukono. We use available data to define the spatial, geographical and demographic environment of the agent based system. The spatial and geographical environment is captured using a network structure. The nodes of the network represent the GIS locations of villages in Uganda. The links between the village nodes correspond to the available road infrastructures which are primarily dirt road or paved/gravel roads.

Transport Cost and Infrastructure Investments

The trade reforms initiated in Uganda during the 1990s have gradually led to trade liberalisation aiming to improve competitiveness and reduce distortion in the markets. For instance, the granting of preferential treatment in the form of duty reductions to COMISA countries, and the East African Community Custom Union treaty signed in 2004 are two pro-trade initiatives that have helped lower the barriers to trade, such as restrictions and tariff controls, and have significantly increased the volume of traffic of non-traditional agriculture exports. The amount of non-traditional agriculture exports has risen strongly from 14% in 1990 to over 68% in 2012, in line with Uganda’s annual average real GDP growth rate of 7.5% over the last 25 years (Uganda Government Statistics 2013). Strong export rates convincingly show us that agriculture production is pivotal in the economic growth of Uganda.

Synthesis: The Integrated Multilayer Model

The purpose of this chapter is to integrate the previous chapters in relation to policy analysis. We start by examining the vertical interactions within the Multilayer model framework, and in doing so, we return to hypothesis 3: How can trade be facilitated and investment and growth take place by leveraging transport logistics accessibility as well as the economic and sociological factors of the SPICs? In Chap. 5, we discussed on the basis of hypothesis 3, how to evaluate Port Attractiveness in order to foster economic growth and investment. Thereafter in Chaps. 6 and 7, we analysed the topological structure of the networks and identified the impacts of economies of scale and trade agreements. Our next endeavour is to integrate these different layers by connecting the factors already highlighted in the Port Attractiveness Index, and above all to test the analyses and results obtained in Chaps. 5, 6 and 7 for the networks.