The use of strategic visioning to enhance local tourism planning in periphery communities

Tourism has had a profound impact upon destinations worldwide, and although this impact has been positive for many destinations, there are numerous examples where tourism has adversely impacted upon the environment and social fabric of the destination community (Coccossis 1996; Murphy 1985). The negative impacts of tourism have been attributed, among other things, to inadequate or non-existent planning for development (Gunn 1994; Hall2000). This has led to increased calls for tourism planning to offset some of the negative impacts that tourism can have on the destination community. While a number of approaches have been advocated, a collaborative philosophy, based on the principles of sustainability, is more likely to result in acceptable and successful policies and programmes for tourism destinations (Farrell1986; Jamal & Getz 1995; Maitland 2002; Minca & Getz 1995). Such an approach focuses on cooperation and broader based participation in tourism planning and decision-making between stakeholders to lead to agreement on planning directions and goals, with one of the primary objectives of collaborative arrangements being to develop a strategic vision for a destination (Bramwell & Lane 2000). [Extract from introduction]

Development and implementation of the population Fisher information matrix for the evaluation of population pharmacokinetic designs.

In population pharmacokinetic studies, the precision of parameter estimates is dependent on the population design. Methods based on the Fisher information matrix have been developed and extended to population studies to evaluate and optimize designs. In this paper we propose simple programming tools to evaluate population pharmacokinetic designs. This involved the development of an expression for the Fisher information matrix for nonlinear mixed-effects models, including estimation of the variance of the residual error. We implemented this expression as a generic function for two software applications: S-PLUS and MATLAB. The evaluation of population designs based on two pharmacokinetic examples from the literature is shown to illustrate the efficiency and the simplicity of this theoretic approach. Although no optimization method of the design is provided, these functions can be used to select and compare population designs among a large set of possible designs, avoiding a lot of simulations.