1,721,797 research outputs found
Planning Models: Scoping the Scene
No full textPlanning is a massive field, and the types and variations of models used are enormous. This chapter (and the related collection), therefore, makes no pretence to cover all the various forms of models that are used across the entire gambit of planning applications. It focuses very much on the types of mathematical models that have been developed by social scientists in their efforts to explain, in particular, spatial location patterns. We feel that it is preferable to systematically treat an area of modelling in a particular context rather than pursue a shot-gun approach embracing a a number of planning applications which would mean the pellets spreading across a large area without any real penetration at any point
Complexity and Spatial Networks
No full textIn the past decade, complexity has become an important and fascinating domain for advanced research on non-linear dynamics, in which a multiplicity of scientific fields are involved (physics, life sciences, social sciences, economics, geography, and so forth). Complex systems analysis refers to research at the dynamic interface of – or the interaction between – small or micro-elements of a system that are interconnected and determine a macro-level of operation of the system that is not just the sum of the micro-elements. As a result of self-organizing forces among interacting micro-units, a dynamic network configuration may emerge that displays its own dynamics, ranging from ‘butterfly’ effects to scale-free evolution, or from bifurcations with unexpected phase transitions to preferential attachment in small-world networks. The complexity movement has also had far-reaching impacts on dynamics research in the spatial sciences.
The space-economy is often interpreted as a standard well-functioning economic system enriched with the element of space. But space is not just an additional dimension of the economy: it forms an intrinsic feature of any geographic-economic system and may lead to the emergence of complex non-linear and interactive behaviours and processes in a geographic setting. The foundation for an interpretation of the space-economy as an interdependent complex set of economic relationships – at different geographic scales and with a variety of time dimensions involved – can be found in the ‘first law of geography’ formulated by Tobler (1970) who stipulates that everything in space is related to everything else, but near things are more related than distant things. The solidity of this law needs to be reconsidered in the light of recent advances in complexity and network theory. In particular, the latest findings in network theory show how – for certain network typologies – distant things can be related by means of ‘hubs’ or ‘egos’ (preferential nodes/attractors). Spatial networks appear to exert a dynamic impact on an organized space.
One of the striking features in the modern space-economy has been the simultaneous occurrence of spatial dynamics (both fast and slow dynamics) and spatial inertia (e.g. persistent welfare disparities between regions). Regions and cities are apparently operating in a complex force field, with asynchronously emerging key factors that impact on regional or urban development in different ways and with different growth paces.
This rapidly changing scene of regional and urban development has called for new research departures, such as: a reliance on experimental psychology/sociology, design of learning principles for decision makers (based on evolutionary biology), integration of ethical and sociological notions in policies for a multi-cultural society, etc. Consequently, regional and urban research has become richer in scope, with more emphasis on interdisciplinarity, complexity, synergy among research methodologies, conflict management principles, adaptive and evolutionary (notably, learning) behaviour, and increasing interest in the great potential offered by the cognitive sciences.
This book aims to offer a panoramic view of recent advances in spatial complexity, in order to enhance our understanding of complex spatial networks by simplicity in terms of the basic driving forces of systemic impacts, as well as in terms of modelling such systems. Simple models mapping out the evolution of complex networks are undoubtedly a key issue in spatial economic research
Spatial Data Clustering and Self-Organized Criticality: Empirical Experiments on Regional Labour Market Dynamics
No full textSelf-Organised Criticality (SOC) is a recently developed concept from dynamic systems analysis that aims to investigate the transition trajectories of evolutionary systems. In order to detect one of the most evident characterisations of SOC, viz. the emergence of a power law distribution mapping out the “avalanches” in complex networks, data clustering is necessary. There are, however, several clustering techniques available. In a practical context, the choice of the clustering method adopted is often left to the analyst.
Starting from the above considerations, the present chapter aims to investigate a new methodological challenge, by investigating the influence of the specific clustering method adopted on the results representing the SOC state. Our empirical application concerns the developmental patterns of regional labour markets in Germany. The chapter explores the evolutionary dynamics of employment at a district level in West Germany, as well as in the combined West and East German case, by considering different types of clustering methods. The comparative analysis performed on the basis of these distinct clustering methods suggests, in general, the existence of a power law distribution, and hence of a critical state in the network under consideration. It is noteworthy that the evolution of the biggest avalanches shows clear regional differences between the West and East German labour markets
Spatial Filtering Methods for Tracing Space-Time Developments in an Open Regional System: Experiments with German Unemployment Data
Full text linkSocio-economic interrelationships among regions can be measured in terms of economic flows, migration, or physical geographically-based measures, such as distance or length of shared areal unit boundaries. In general, proximity and openness tend to favour a similar economic performance among adjacent regions. Therefore, proper forecasting of socio-economic variables, such as employment, requires an understanding of spatial (or spatio-temporal) autocorrelation effects associated with a particular geographic configuration of a system of regions. Several spatial econometric techniques have been developed in recent years to identify spatial interaction effects within a parametric framework. Alternatively, newly devised spatial filtering techniques aim to achieve this end as well through the use of a semi-parametric approach. The experiments presented in this paper deal with the analysis of and accounting for spatial autocorrelation by means of spatial filtering techniques for data pertaining to regional unemployment in Germany. The available dataset comprises information about the share of unemployed workers in 439 German districts (the NUTS-III regional aggregation level). In this paper, various results based upon an eigenvector spatial filter model formulation (that is, the use of orthogonal map pattern components), constructed for the 439 German districts, are presented, with an emphasis on their consistency over several observation years. New insights obtained by applying spatial filtering to the database about the German regional labour markets also are discussed
Territorial capital and regional growth: increasing returns in cognitive knowledge use
No full textKnowledge drives the growth of nations and regions in a competitive space-economy. Hence, we would expect a strong correlation between investments in R&D, knowledge and learning processes, on the one hand, and productivity increases, on the other. However, the empirical evidence shows consistent discrepancies between knowledge inputs and economic performance across geographical units. This paper addresses this intriguing issue at the regional level, by highlighting both theoretically and empirically the strategic importance played by cognitive elements as part of “territorial capital” in mediating between knowledge production and regional growth. The main proposition of the paper, subject to empirical testing, is that cognitive elements as part of territorial capital magnify the contribution of knowledge by determining the formation of increasing returns to knowledge exploitation
Travel Motivations of Seniors: A Review and a Meta-Analytical Assessment
Full text linkOver the past decades, leisure travel has become increasingly popular in older segments of the world population, as a consequence of global factors such as a rise in life expectancy, improved health conditions, a higher disposable income, and increased availability of discretionary time in retirement age. Consequently, researchers have become more interested
in studying the motivations for travel of seniors. A number of questions may be raised or have been addressed in the recent past: What are the main factors explaining the travelling choices of seniors? Are their travel motivations different from the ones of the younger population, which have been widely studied in the past? Are geographical differences in terms of
motivations comparable between different age groups? Why is senior tourism a topic of particular interest with regard to Asia? In order to answer such questions, in this paper we provide a review of the literature on the travel motivations of seniors. On the basis of 29 articles published on the topic, we provide a qualitative and meta-analytic assessment of past
findings, by investigating the dimensions of travel motivations most frequently employed in past seniors surveys. Finally, we discuss a research agenda for further analysis of senior travel motivations and for the integration of this branch of travel research within a wider framework
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