The Experts below are selected from a list of 216 Experts worldwide ranked by ideXlab platform
Jorge Mateu - One of the best experts on this subject based on the ideXlab platform.
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Analysis of forest thinning strategies through the development of space–time growth–Interaction simulation Models
Stochastic Environmental Research and Risk Assessment, 2009Co-Authors: Eric Renshaw, Carlos Comas, Jorge MateuAbstract:Thinning strategies are a prime factor in generating Spatial patterns in managed forests, and have a dramatic effect on stand development, and hence product yields. As trees generally have long life spans relative to the length of typical research projects, the design and analysis of complex long-term Spatial–temporal experiments in forest stands is clearly difficult. This means that forest Modelling is a key tool in the formulation and development of optimal management strategies. We show that the highly flexible Renshaw and Särkkä algorithm for Modelling the space–time development of marked point processes is easily adapted to enable the comparative study of different thinning regimes. This procedure not only provides a powerful descriptor of forest stand growth, but there is considerable evidence that it is particularly robust to the accuracy of Model choice. Two distinct thinning approaches are considered in conjunction with a variety of tree growth functions and both hard- and soft-core Interaction functions. The results obtained strongly suggest that combining the immigration–growth–Spatial Interaction Model with Spatially explicit thinning algorithms produces a realistic and flexible mechanism for mimicking real forest scenarios.
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analysis of forest thinning strategies through the development of space time growth Interaction simulation Models
Stochastic Environmental Research and Risk Assessment, 2009Co-Authors: Eric Renshaw, Carlos Comas, Jorge MateuAbstract:Thinning strategies are a prime factor in generating Spatial patterns in managed forests, and have a dramatic effect on stand development, and hence product yields. As trees generally have long life spans relative to the length of typical research projects, the design and analysis of complex long-term Spatial–temporal experiments in forest stands is clearly difficult. This means that forest Modelling is a key tool in the formulation and development of optimal management strategies. We show that the highly flexible Renshaw and Sarkka algorithm for Modelling the space–time development of marked point processes is easily adapted to enable the comparative study of different thinning regimes. This procedure not only provides a powerful descriptor of forest stand growth, but there is considerable evidence that it is particularly robust to the accuracy of Model choice. Two distinct thinning approaches are considered in conjunction with a variety of tree growth functions and both hard- and soft-core Interaction functions. The results obtained strongly suggest that combining the immigration–growth–Spatial Interaction Model with Spatially explicit thinning algorithms produces a realistic and flexible mechanism for mimicking real forest scenarios.
Eric Renshaw - One of the best experts on this subject based on the ideXlab platform.
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Analysis of forest thinning strategies through the development of space–time growth–Interaction simulation Models
Stochastic Environmental Research and Risk Assessment, 2009Co-Authors: Eric Renshaw, Carlos Comas, Jorge MateuAbstract:Thinning strategies are a prime factor in generating Spatial patterns in managed forests, and have a dramatic effect on stand development, and hence product yields. As trees generally have long life spans relative to the length of typical research projects, the design and analysis of complex long-term Spatial–temporal experiments in forest stands is clearly difficult. This means that forest Modelling is a key tool in the formulation and development of optimal management strategies. We show that the highly flexible Renshaw and Särkkä algorithm for Modelling the space–time development of marked point processes is easily adapted to enable the comparative study of different thinning regimes. This procedure not only provides a powerful descriptor of forest stand growth, but there is considerable evidence that it is particularly robust to the accuracy of Model choice. Two distinct thinning approaches are considered in conjunction with a variety of tree growth functions and both hard- and soft-core Interaction functions. The results obtained strongly suggest that combining the immigration–growth–Spatial Interaction Model with Spatially explicit thinning algorithms produces a realistic and flexible mechanism for mimicking real forest scenarios.
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analysis of forest thinning strategies through the development of space time growth Interaction simulation Models
Stochastic Environmental Research and Risk Assessment, 2009Co-Authors: Eric Renshaw, Carlos Comas, Jorge MateuAbstract:Thinning strategies are a prime factor in generating Spatial patterns in managed forests, and have a dramatic effect on stand development, and hence product yields. As trees generally have long life spans relative to the length of typical research projects, the design and analysis of complex long-term Spatial–temporal experiments in forest stands is clearly difficult. This means that forest Modelling is a key tool in the formulation and development of optimal management strategies. We show that the highly flexible Renshaw and Sarkka algorithm for Modelling the space–time development of marked point processes is easily adapted to enable the comparative study of different thinning regimes. This procedure not only provides a powerful descriptor of forest stand growth, but there is considerable evidence that it is particularly robust to the accuracy of Model choice. Two distinct thinning approaches are considered in conjunction with a variety of tree growth functions and both hard- and soft-core Interaction functions. The results obtained strongly suggest that combining the immigration–growth–Spatial Interaction Model with Spatially explicit thinning algorithms produces a realistic and flexible mechanism for mimicking real forest scenarios.
Carlos Comas - One of the best experts on this subject based on the ideXlab platform.
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Analysis of forest thinning strategies through the development of space–time growth–Interaction simulation Models
Stochastic Environmental Research and Risk Assessment, 2009Co-Authors: Eric Renshaw, Carlos Comas, Jorge MateuAbstract:Thinning strategies are a prime factor in generating Spatial patterns in managed forests, and have a dramatic effect on stand development, and hence product yields. As trees generally have long life spans relative to the length of typical research projects, the design and analysis of complex long-term Spatial–temporal experiments in forest stands is clearly difficult. This means that forest Modelling is a key tool in the formulation and development of optimal management strategies. We show that the highly flexible Renshaw and Särkkä algorithm for Modelling the space–time development of marked point processes is easily adapted to enable the comparative study of different thinning regimes. This procedure not only provides a powerful descriptor of forest stand growth, but there is considerable evidence that it is particularly robust to the accuracy of Model choice. Two distinct thinning approaches are considered in conjunction with a variety of tree growth functions and both hard- and soft-core Interaction functions. The results obtained strongly suggest that combining the immigration–growth–Spatial Interaction Model with Spatially explicit thinning algorithms produces a realistic and flexible mechanism for mimicking real forest scenarios.
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analysis of forest thinning strategies through the development of space time growth Interaction simulation Models
Stochastic Environmental Research and Risk Assessment, 2009Co-Authors: Eric Renshaw, Carlos Comas, Jorge MateuAbstract:Thinning strategies are a prime factor in generating Spatial patterns in managed forests, and have a dramatic effect on stand development, and hence product yields. As trees generally have long life spans relative to the length of typical research projects, the design and analysis of complex long-term Spatial–temporal experiments in forest stands is clearly difficult. This means that forest Modelling is a key tool in the formulation and development of optimal management strategies. We show that the highly flexible Renshaw and Sarkka algorithm for Modelling the space–time development of marked point processes is easily adapted to enable the comparative study of different thinning regimes. This procedure not only provides a powerful descriptor of forest stand growth, but there is considerable evidence that it is particularly robust to the accuracy of Model choice. Two distinct thinning approaches are considered in conjunction with a variety of tree growth functions and both hard- and soft-core Interaction functions. The results obtained strongly suggest that combining the immigration–growth–Spatial Interaction Model with Spatially explicit thinning algorithms produces a realistic and flexible mechanism for mimicking real forest scenarios.
Yao Shen - One of the best experts on this subject based on the ideXlab platform.
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segregation through space a scope of the flow based Spatial Interaction Model
Journal of Transport Geography, 2019Co-Authors: Yao ShenAbstract:Abstract People are socially divided through urban space, where they experience segregation dynamically. By conceptualising mobilised social inclusion as the gravitational Interactions between urban human flow patterns, this article introduces a framework for measuring the extent to which two trajectories interact with one another in daily activity space, in which a series of indices, theoretically equivalent to those developed in segregation research, are produced to capture the Interaction potentials among various social groups from different perspectives. These scopes include absolute, relative and multi-group using pairs of places as analysis units, as well as place-based measurements that are very sensitive to the Spatial configuration of the flow-based Spatial Interaction potentials. The application in the case of Greater London implies that the relative indices capture the Spatial differentiation among various modes of Interactions, portraying the between-domains exposure levels might be experienced by different occupations when they commute across places. The study demonstrates that mobilised Interaction is influenced by between-domains mobility, and the proposed approach can provide a network understanding of social exposure through the edges between every two place nodes, going beyond existing place-based measurements. In addition, the changes between place-based results aggregated by origins and those determined by destinations showcase the dynamic shift of in-site exposure during peak hours. Though only commuting behaviours are demonstrated in this work, the framework introduced can be easily extended to the Spatial Interactions between any flow trajectories for any Spatial unit, e.g., place (point-wise), place pairs (pair-wise), or specified routes (path-wise), within the activity space defined by time geography or the life-course domain approach.
Manfred M. Fischer - One of the best experts on this subject based on the ideXlab platform.
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Barriers to cross-region research and development collaborations in Europe: evidence from the fifth European Framework Programme
Annals of Regional Science, 2015Co-Authors: Aurélien Fichet De Clairfontaine, Manfred M. Fischer, Rafael Lata, Manfred PaierAbstract:The focus of this paper is on cross-region R&D collaboration funded by the fifth EU Framework Programme (FP5). The objective is to measure distance, institutional, language and technological barrier effects that may hamper collaborative activities between European regions. Particular emphasis is laid on measuring discrepancies between two types of collaborative R&D activities, those generating output in terms of scientific publications and those that do not. The study area is composed of 255 NUTS-2 regions that cover the pre-2007 member states of the European Union (excluding Malta and Cyprus) as well as Norway and Switzerland. We employ a negative binomial Spatial Interaction Model specification to address the research question, along with an eigenvector Spatial filtering technique suggested by Fischer and Griffith (2008) to account for the presence of network autocorrelation in the origin–destination cooperation data. The study provides evidence that the role of geographical distance as collaborative deterrent is significantly lower if collaborations generate scientific output. Institutional barriers do not play a significant role for collaborations with scientific output. Language and technological barriers are smaller but the estimates indicate no significant discrepancies between the two types of collaborative R&D activities that are in focus of this study. Copyright Springer-Verlag Berlin Heidelberg 2015
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Barriers to cross-region research and development collaborations in Europe. Evidence from the fifth European Framework Programme
SSRN Electronic Journal, 2014Co-Authors: Aurélien Fichet De Clairfontaine, Manfred M. Fischer, Rafael Lata, Manfred PaierAbstract:The focus of this paper is on cross-region R&D collaboration funded by the fifth EU Framework Programme (FP5). The objective is to measure distance, institutional, language and technological barrier effects that may hamper collaborative activities between European regions. Particular emphasis is laid on measuring discrepancies between two types of collaborative R&D activities, those generating output in terms of scientific publications and those that do not. The study area is composed of 255 NUTS-2 regions that cover the pre-2007 member states of the European Union (excluding Malta and Cyprus) as well as Norway and Switzerland. We employ a negative binomial Spatial Interaction Model specification to address the research question, along with an eigenvector Spatial filtering technique suggested by Fischer and Griffith (2008) to account for the presence of network autocorrelation in the origin–destination cooperation data. The study provides evidence that the role of geographical distance as collaborative deterrent is significantly lower if collaborations generate scientific output. Institutional barriers do not play a significant role for collaborations with scientific output. Language and technological barriers are smaller but the estimates indicate no significant discrepancies between the two types of collaborative R&D activities that are in focus of this study.
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Spatial regression based Model specifications for exogenous and endogenous Spatial Interaction
ERSA conference papers, 2014Co-Authors: Manfred M. Fischer, James P. LesageAbstract: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.
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Spatial Interaction Models and Spatial Dependence
Spatial Data Analysis, 2011Co-Authors: Manfred M. Fischer, Jinfeng WangAbstract:Spatial Interaction Models of the types discussed in the previous chapter take the view that inclusion of a Spatial separation function between origin and destination locations is adequate to capture any Spatial dependence in the sample data. LeSage and Pace (J Reg Sci 48(5):941–967, 2008), and Fischer and Griffith (J Reg Sci 48(5):969–989, 2008) provide theoretical as well as an empirical motivation that this may not be adequate to Model potentially rich patterns that can arise from Spatial dependence. In this chapter we consider three approaches to deal with Spatial dependence in origin–destination flows. Two approaches incorporate Spatial correlation structures into the independence (log-normal) Spatial Interaction Model. The first specifies a (first order) Spatial autoregressive process that governs the Spatial Interaction variable (see LeSage and Pace (J Reg Sci 48(5):941–967, 2008)). The second approach deals with Spatial dependence by specifying a Spatial process for the disturbance terms, structured to follow a (first order) Spatial autoregressive process. In this framework, the Spatial dependence resides in the disturbance process (see Fischer and Griffith (J Reg Sci 48(5):969–989, 2008)). A final approach relies on using a Spatial filtering methodology developed by Griffith (Spatial autocorrelation and Spatial filtering, Springer, Berlin, Heidelberg and New York, 2003) for area data, and leads to eigenfunction based Spatial filtering specifications of both the log-normal and the Poisson Spatial Interaction Model versions (see Fischer and Griffith (J Reg Sci 48(5):969–989, 2008)).
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knowledge spillovers across europe evidence from a poisson Spatial Interaction Model with Spatial effects
Papers in Regional Science, 2007Co-Authors: James P. Lesage, Manfred M. Fischer, Thomas ScherngellAbstract:This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital Model proposed by Griliches (1979), augmented with a Spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy regional knowledge capital stocks for N=203 regions over the 1997- 2002 time period. In estimating the effects we implement a Spatial panel data Model that controls for the Spatial autocorrelation due to neighbouring regions and the individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and add an important Spatial dimension to the discussion, by showing that productivity effects of knowledge spillovers increase with geographic proximity. (authors' abstract)