2,472 research outputs found

    GIS and Network Analysis

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    Both geographic information systems (GIS) and network analysis are burgeoning fields, characterised by rapid methodological and scientific advances in recent years. A geographic information system (GIS) is a digital computer application designed for the capture, storage, manipulation, analysis and display of geographic information. Geographic location is the element that distinguishes geographic information from all other types of information. Without location, data are termed to be non-spatial and would have little value within a GIS. Location is, thus, the basis for many benefits of GIS: the ability to map, the ability to measure distances and the ability to tie different kinds of information together because they refer to the same place (Longley et al., 2001). GIS-T, the application of geographic information science and systems to transportation problems, represents one of the most important application areas of GIS-technology today. While traditional GIS formulation's strengths are in mapping display and geodata processing, GIS-T requires new data structures to represent the complexities of transportation networks and to perform different network algorithms in order to fulfil its potential in the field of logistics and distribution logistics. This paper addresses these issues as follows. The section that follows discusses data models and design issues which are specifically oriented to GIS-T, and identifies several improvements of the traditional network data model that are needed to support advanced network analysis in a ground transportation context. These improvements include turn-tables, dynamic segmentation, linear referencing, traffic lines and non-planar networks. Most commercial GIS software vendors have extended their basic GIS data model during the past two decades to incorporate these innovations (Goodchild, 1998). The third section shifts attention to network routing problems that have become prominent in GIS-T: the travelling salesman problem, the vehicle routing problem and the shortest path problem with time windows, a problem that occurs as a subproblem in many time constrained routing and scheduling issues of practical importance. Such problems are conceptually simple, but mathematically complex and challenging. The focus is on theory and algorithms for solving these problems. The paper concludes with some final remarks.


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    This paper presents a theoretical neoclassical growth model with two kinds of capital, and technological interdependence among regions. Technological interdependence is assumed to operate through spatial externalities caused by disembodied knowledge diffusion between technologically similar regions. The transition from theory to econometrics yields a reduced-form empirical model that in the spatial econometrics literature is known as spatial Durbin model. Technological dependence between regions is formulated by a connectivity matrix that measures closeness of regions in a technological space spanned by 120 distinct technological fields. We use a system of 158 regions across 14 European countries over the period from 1995 to 2004 to empirically test the model. The paper illustrates the importance of an impact-based model interpretation, in terms of the LeSage and Pace (2009) approach, to correctly quantify the magnitude of spillover effects that avoid incorrect inferences about the presence or absence of significant capital externalities among technologically similar regions.Economic growth, augmented Mankiw-Romer-Weil model, disembodied knowledge diffusion, technological similarity between regions, spatial econometrics, European regions

    The innovation process and network activities of manufacturing firms: Conceptual considerations and empirical evidence from the metropolitan region of Vienna

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    Innovation networks of manufacturers are currently receiving much attention as a competitive strategy. The contribution represents a response to the growing recognition that there are very important reasons why we need a better understanding of the relationship between innovation and networking. This response is conceptual in form, but enriched with some empirical evidence from the metropolitan region of Vienna. The paper demonstrates unambiguously the importance of external network activities during the innovation process that are organized around five types of networks: customer networks, manufacturing supplier networks, producer service supplier networks, producer networks and co-operation with research institutions and departments of universities. The data clearly indicate that networking is not only and primarily a metropolitan phenomenon. Spatial proximity is just one, but evidently not the decisive criterion for innovation-oriented relationships. The geography of networking largely extends to national and international levels.

    Network dependence in multi-indexed data on international trade flows

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    Faced with the problem that conventional multidimensional fixed effects models only focus on unobserved heterogeneity, but ignore any potential cross-sectional dependence due to network interactions, we introduce a model of trade flows between countries over time that allows for network dependence in flows, based on sociocultural connectivity structures. We show that conventional multidimensional fixed effects model specifications exhibit cross-sectional dependence between countries that should be modeled to avoid simultaneity bias. Given that the source of network interaction is unknown, we propose a panel gravity model that examines multiplenetwork interaction structures, using Bayesian model probabilities to determine those most consistent with the sample data. This is accomplished with the use of computationally efficient Markov Chain Monte Carlo estimation methods that produce a Monte Carlo integration estimate of the log-marginal likelihood that can be used for model comparison. Application of the model to a panel of trade flows points to network spillover effects, suggesting the presence of network dependence and biased estimates from conventional trade flow specifications. The most important sources of network dependence were found to be membership in trade organizations, historical colonial ties, common currency, and spatial proximity of countries.Series: Working Papers in Regional Scienc

    Production of Knowledge and Geographically Mediated Spillovers from Universities: Spatial Econometric Perspective and Evidence from Austria

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    The paper sheds some light on the issue of geographically mediated knowledge spillovers from university research activities to regional knowledge production in the high tech sector in Austria. Knowledge spillovers occur because knowledge created by university is typically not contained within that institution, and thereby creates value for others. The conceptual framework for analysing geographic spillovers of university research on regional knowledge production is derived from Griliches (1979). It is assumed that knowledge production in the high tech sector essentially depends on two major sources of knowledge: the university research that represents the potential pool of knowledge spillovers and R&D performed by the high tech sector itself. Knowledge is measured in terms of patents, university research and R&D in terms of expenditures. We refine the standard %0D knowledge production function by modelling research spillovers as a spatially discounted external stock of knowledge. This enables us to capture local and interlocal spillovers. Using district-level data and employing spatial econometric tools evidence is found of university research spillovers that transcend the geographic scale of the political district in Austria. It is shown that geographic boundedness of the spillovers is linked to a decay effect. Reference Griliches Z. (1979): Issues in Assessing the Contribution of Research and Development to Productivity Growth, Bell Journal of Economics 10, 92-116

    Neural Network Modelling of Constrained Spatial Interaction Flows

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    Fundamental to regional science is the subject of spatial interaction. GeoComputation - a new research paradigm that represents the convergence of the disciplines of computer science, geographic information science, mathematics and statistics - has brought many scholars back to spatial interaction modeling. Neural spatial interaction modeling represents a clear break with traditional methods used for explicating spatial interaction. Neural spatial interaction models are termed neural in the sense that they are based on neurocomputing. They are clearly related to conventional unconstrained spatial interaction models of the gravity type, and under commonly met conditions they can be understood as a special class of general feedforward neural network models with a single hidden layer and sigmoidal transfer functions (Fischer 1998). These models have been used to model journey-to-work flows and telecommunications traffic (Fischer and Gopal 1994, Openshaw 1993). They appear to provide superior levels of performance when compared with unconstrained conventional models. In many practical situations, however, we have - in addition to the spatial interaction data itself - some information about various accounting constraints on the predicted flows. In principle, there are two ways to incorporate accounting constraints in neural spatial interaction modeling. The required constraint properties can be built into the post-processing stage, or they can be built directly into the model structure. While the first way is relatively straightforward, it suffers from the disadvantage of being inefficient. It will also result in a model which does not inherently respect the constraints. Thus we follow the second way. In this paper we present a novel class of neural spatial interaction models that incorporate origin-specific constraints into the model structure using product units rather than summation units at the hidden layer and softmax output units at the output layer. Product unit neural networks are powerful because of their ability to handle higher order combinations of inputs. But parameter estimation by standard techniques such as the gradient descent technique may be difficult. The performance of this novel class of spatial interaction models will be demonstrated by using the Austrian interregional traffic data and the conventional singly constrained spatial interaction model of the gravity type as benchmark. References Fischer M M (1998) Computational neural networks: A new paradigm for spatial analysis Environment and Planning A 30 (10): 1873-1891 Fischer M M, Gopal S (1994) Artificial neural networks: A new approach to modelling interregional telecommunciation flows, Journal of Regional Science 34(4): 503-527 Openshaw S (1993) Modelling spatial interaction using a neural net. In Fischer MM, Nijkamp P (eds) Geographical information systems, spatial modelling, and policy evaluation, pp. 147-164. Springer, Berlin

    A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data

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    Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.

    The tyranny of regional unemployment rates

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    Although there is a substantial body of literature on labour market analysis, most of it ignores the spatial dimension of the labour market. A spatial perspective in analysing labour market processes is important for several reasons. FIRST, labour markets are by no means as homogeneous as conventional labour market theories assume. SECOND, most countries are displaying strong regional variations in the dynamics of unemployment. THIRD, geographical space exerts a frictional effect on labour market processes. Regional unemployment rates appear to be the most important indicators for analysing labour market processes from a spatial perspective. The paper aims to discuss some of the problems that are associated with the use of regional unemployment rates. We will focus attention on conceptual problems, problems of data quality and on some of the new problems that have arisen due to the widespread use of new computer technology. Solutions to many of the problems are obvious, but many of the new problems will require some extra effort for their solution. The tyranny that threatens the research community is that regional unemployment data exercise a power over us that can lead the naive to misinterpretations. The data may mislead even the most righteous among us. A good deal of research effort is often given to overcome the tyranny that is found in the columns and rows that the lay public likes to call statistics. The discussion will be enriched by means of a study utilizing regional unemployment rates at the district level in West Germany.

    Regional Income Convergence in the Enlarged Europe, 1995-2000: A Spatial Econometric Perspective

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    This paper adopts a spatial econometric perspective to analyse regional convergence of per capita income in Europe in 1995 to 2000 and, moreover, relaxes the assumption of a single steady-state growth path which appears to be out of tune with reality of empirical dynamics. The two-club spatial error convergence model with groupwise heteroskedasticity is found to be most appropriate for the data at hand. Two empirical key findings are worthwhile to note. The first is that the data provide much support for unconditional Ăź-convergence in Europe. The second is that the usual convergence conclusions hold. But they do so for reasons that are not revealed by the classical test equation that is typical in mainstream economics literature. --European Regions,Income Convergence,Spatial Econometrics
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