7 resultados para regional coverage

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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How immigration affects the labor market of the host country is a topic of major concern for many immigrant-receiving nations. Spain is no exception following the rapid increase in immigrant flows experienced over the past decade. We assess the impact of immigration on Spanish natives’ income by estimating the net immigration surplus accruing at the national level and at high immigrant-receiving regions while taking into account the imperfect substitutability of immigrant and native labor. Specifically, using information on the occupational densities of immigrants and natives of different skill levels, we develop a mapping of immigrant-to-native self-reported skills that reveals the combination of natives across skills that would be equivalent to an immigrant of a given self-reported skill level, which we use to account for any differences between immigrant self-reported skill levels and their effective skills according to the Spanish labor market. We find that the immigrant surplus amounts to 0.04 percent of GDP at the national level and it is even higher for some of the main immigrant-receiving regions, such as Cataluña, Valencia, Madrid, and Murcia.

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[ES] Este trabajo analiza la relación entre el desarrollo regional y la creación de empresas desde una perspectiva micro del enfoque institucional, a partir de los stakeholders más relevantes que intervienen en el proceso. La contribución de los emprendedores al crecimiento económico regional viene siendo objeto de especial atención por los poderes públicos, para lo que se necesita un sistema de referencias que permita evaluar la adecuación de los programas públicos de fomento de la actividad emprendedora.

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Análisis de la situación laboral de la comunidad de Madrid, realizando una descripción de la evolución del panorama laboral en dicha región y comparando ésta con otros ámbitos geográficos, así como con otros contextos económicos. Utilizando datos de la EPA. Idioma: Español

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Coherent ecological networks (EN) composed of core areas linked by ecological corridors are being developed worldwide with the goal of promoting landscape connectivity and biodiversity conservation. However, empirical assessment of the performance of EN designs is critical to evaluate the utility of these networks to mitigate effects of habitat loss and fragmentation. Landscape genetics provides a particularly valuable framework to address the question of functional connectivity by providing a direct means to investigate the effects of landscape structure on gene flow. The goals of this study are (1) to evaluate the landscape features that drive gene flow of an EN target species (European pine marten), and (2) evaluate the optimality of a regional EN design in providing connectivity for this species within the Basque Country (North Spain). Using partial Mantel tests in a reciprocal causal modeling framework we competed 59 alternative models, including isolation by distance and the regional EN. Our analysis indicated that the regional EN was among the most supported resistance models for the pine marten, but was not the best supported model. Gene flow of pine marten in northern Spain is facilitated by natural vegetation, and is resisted by anthropogenic landcover types and roads. Our results suggest that the regional EN design being implemented in the Basque Country will effectively facilitate gene flow of forest dwelling species at regional scale.

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This document aims to describe an update of the implementation of the J48Consolidated class within WEKA platform. The J48Consolidated class implements the CTC algorithm [2][3] which builds a unique decision tree based on a set of samples. The J48Consolidated class extends WEKA’s J48 class which implements the well-known C4.5 algorithm. This implementation was described in the technical report "J48Consolidated: An implementation of CTC algorithm for WEKA". The main, but not only, change in this update is the integration of the notion of coverage in order to determine the number of samples to be generated to build a consolidated tree. We define coverage as the percentage of examples of the training sample present in –or covered by– the set of generated subsamples. So, depending on the type of samples that we use, we will need more or less samples in order to achieve a specific value of coverage.