ISHS VII International Symposium on Orchard and Plantation Systems NEW DWARFING AND SEMI-DWARFING PILLNITZ APPLE AND PEAR ROOTSTOCKS
The proliferation and dissemination of digital spatial databases, coupled with the ever wider use of Geographic Information Systems [GIS] and Remote Sensing [RS] data, is stimulating increasing interest in spatial analysis from outside the spatial sciences. The...
It has become increasingly recognised that the location of economic activities cannot be properly understood in isolation from its wider socio-economic context, and, thus, we cannot understand regional development without linking it to the epochal transition to...
In this paper a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedure for parameter estimation, such as the Alopex procedure. A benchmark comparison against the standard origin constrained gravity model and the two–stage neural network approach, suggested by Openshaw (1998), illustrates the superiority of the proposed model in terms of the generalization performance measured by ARV and SRMSE.
This paper addresses the effect of liberalisation and transformation in Eastern Europe on international trade flows between specific West and East European countries belonging to the Rhine-Main-Danube area. The discussion is focused on volume and direction of trade effects using a simple, but evidently robust model of bilateral trade flows. The estimates are naturally subject to a great range of uncertainty with respect to the likely developments in each of the countries concerned. The numbers might end up wide of the mark in the long term, but we think they are useful nonetheless in helping to frame the issues and focus thinking on the potential impact. The overall trade volumes tend to expand primarily as a result of a rise in living standards and output levels, and increase the openess of the Eastern European countries. Because any increase in income will take time, the global trade import will be spread over a number of years. The collapse of COMECON trading arrangements and the increasing influence of market forces in the Eastern European countries is likely to lead to a reorientation of these countries trade away from each other and towards Western European markets.
This paper focuses on Austrian outbound foreign direct investment (FDI, measured by sales of Austrian affiliates abroad) in Europe over the period 2009–2013, using a spatial Durbin panel data model specification with fixed effects, and a spatial weight matrix based on the first-order contiguity relationship of the countries and normalised by its largest eigenvalue. Third-country effects essentially enter the empirical analysis in two major ways: first, by the endogenous spatial lag on FDI (measured by FDI into markets nearby the host country), and, second, by including an exogenous market potential variable that measures the size of markets nearby the FDI host country in terms of gross domestic product. The question whether the empirical result is compatible with horizontal, vertical, export-platform or complex vertical FDI then depends on the sign and significance levels of both the coefficient of the spatial lag on FDI and the direct impact estimate of the market potential variable. The paper yields robust results that provide significant empirical evidence for horizontal FDI as the main driver of Austrian outbound FDI in Europe. This result is strengthened by the indirect impact estimate of the market potential variable indicating that spatial spillovers do not matter.
Past focus in the panel gravity literature has been on multidimensional fixed effects specifications in an effort to accommodate heterogeneity. After introducing fixed effects for each origin- destination dyad and time-period speciffic effects, we find evidence of cross-sectional dependence in flows. We propose a simultaneous dependence gravity model that allows for network dependence in flows, along with computationally efficient MCMC estimation methods that produce a Monte Carlo integration estimate of log-marginal likelihood useful 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 focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific and time-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.
Modified (sculpted) optical fiber tips for medical delivery systems are described. Tips have aspherical profiles (parabolic, hyperbolic, etc.) Light distributions of modified silica (<i>n</i> = 1.457) tips were calculated using a 3-D ray-tracing program in air and water. It was found that the focusing effects of aspherical tips are from 2.5 to 3.0 times better than those of spherical tips. It is possible to increase the irradiance of tissue in water by about 6 to 9 times using normal silica fiber with high efficiency (without total internal reflection). Experiments were performed in air and water, using registration of light distribution with a CCD-camera, and confirmed the theoretical predictions.
No abstract is provided for this article.
Geographic Information Analysis (GIA) embraces a whole cluster of techniques and models which apply formal, usually mathematical and statistical, structures to systems in which the prime variables of interest vary significantly across space. GIA is currently entering...
Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. T
New economic growth theories may be simply described as an attempt to revive the notion of increasing returns within a theoretical framework that retains the cardinal virtues of the neo-classical systems. Accumulation of knowledge and its spillover into productive...
In this paper we view learning as an unconstrained non-linear minimization problem in which the objective function is defined by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit neural spatial interaction model. We consider Alopex based global search, as opposed to local search based upon backpropagation of gradient descents, each in combination with the bootstrapping pairs approach to solve the maximum likelihood learning problem. Interregional telecommunication traffic flow data from Austria are used as test bed for comparing the performance of the two learning procedures. The study illustrates the superiority of Alopex based global search, measured in terms of Kullback and Leibler’s information criterion.
No abstract is provided for this article.
The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.
Spatial interaction models represent a class of models that are used for modelling origin-destination flow data. The focus of this paper is on the log-normal version of the model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for two different types of spatial dependence in magnitudes. Endogenous interaction reflects situations where there is a reaction to feedback regarding flow magnitudes from regions neighbouring origin and destination regions. This type of interaction can be modelled using specifications proposed by LeSage and Pace (2008) who use spatial lags of the dependent variable to quantify the magnitude and extent of the feedback effects, hence the term endogenous interaction. Exogenous interaction represents a situation where spillovers arise from nearby (or perhaps even distant) regions, and these need to be taken into account when modelling observed variations in flows across the network of regions. In contrast to endogenous interaction, these contextual effects do not generate reactions to the spillovers, leading to a model specification that can be interpreted without considering changes in the long-run equilibrium state of the system of flows. As in the case of social networks, contextual effects are modelled using spatial lags of the explanatory variables that represent characteristics of neighbouring (or more generally connected) regions, but not spatial lags of the dependent variable, hence the term exogenous interaction. In addition to setting forth expressions for the true partial derivatives of non-spatial and endogenous spatial interaction models and associated scalar summary measures from Thomas-Agnan and LeSage (2014), we propose new scalar summary measures for the exogenous spatial interaction specification introduced here. An illustration applies the exogenous spatial interaction model to a flow matrix of teacher movements between 67 school districts in the state of Florida.
