Perceived Diversity of Complex Environmental Systems: Multidimensional Measurement and Synthetic Indicators

The general attitude towards the sustainable management of environmental resources is evolving towards the implementation of ‘participatory’ (as opposed to the classical ‘command and control’) and, especially at local scale, ‘bottom up’ (as opposed to the classical ‘top down’) approaches. This progress pushes a major interest in the development and application of methodologies able to ‘discover’ and ‘measure’ how environmental systems tend to be perceived by the different Stakeholders. Due to the ‘nature’ of the investigated systems, often too ‘complex’ to be treated through a classical deterministic approach, as typical for ‘hard’ physical/mathematical sciences, any ‘measurement’ has necessarily to be multidimensional. In the present report an approach, more typical of ‘soft’ social sciences, is presented and applied to the analysis of the sustainable management of water resources in seven Southern and Eastern Mediterranean Watersheds. The methodology is based on the development and analysis (explorative factor analysis, multidimensional scaling) of a questionnaire and is aimed at the ‘discovery’ and ‘measurement’ of a latent multidimensional ‘underlying structure’ (‘conceptual map’). It is the opinion of the authors, that the identification of a set of ‘consistent’, ‘independent’, ‘bottom up’ and ‘shared’ synthetic indicators (aggregated indices) could be strongly facilitated by the interpretation of the dimensions of the emerging ‘underlying structure’.Participative Approach, Cognitive Map, Factor Analysis, Indicators of Sustainability, Sustainable Water, Management

Macroeconomic effects of demographic change in an OLG model for a small open economy : the case of Belgium

In the absence of behavioural adjustments, demographic change may cut off about 0.4%- point on average from the annual per capita growth rate in the next 25 years. The behavioural responses of households and firms to declining fertility and rising life expectancy may significantly change this outcome, but the sign and the size of this change are unclear. In this paper we construct and parameterize a large-scale OLG model for a small open economy to quantify (the net effect of) these behavioural adjustments. Important endogenous variables in the model are hours worked and (un)employment, investment in human and physical capital, per capita growth and inequality. Individuals differ not only by age, but also by innate ability. We calibrate the model to Belgium and find that it replicates key data since about 1960 remarkably well. Simulating the model, we observe significant (positive) behavioural adjustments by households and firms, but these do not reverse the negative arithmetical effect of projected future demographic change on per capita growth. Many of the adjustments have already taken place in previous decades. Furthermore, ongoing adjustments do not affect future domestic output due to capital outflow in a small open economy. To counter (very) poor per capita growth in the next two decades, policy changes will be necessary

IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees

High-dimensional data with many features are usually challenging to represent with standard visualization techniques. Usually, one has to resort to dimensionality reduction techniques such as PCA, MDS or t-SNE to represent such data. Such dimensionality reduction techniques make it possible to highlight the high-dimensional structures of data. In many of such visualizations, comparable instances appear to form visual clusters. However, no feedback is directly given by these techniques to the user about the features that make the instances cluster together in the visualization. As such, the interpretation of which features define a given visual cluster is a complicated task. In this paper, we propose a novel interactive approach (called Interactive eXplanation of Visual Clusters — IXVC) to explain dimensionality reduction visualizations by mapping their clusters to explanations provided by decision trees. The decision trees use features in high-dimensional data to explain two-dimensional clusters, filling the gap between the dimensionality reduction visualization and the original data

FragViz: visualization of fragmented networks

BACKGROUND Researchers in systems biology use network visualization to summarize the results of their analysis. Such networks often include unconnected components, which popular network alignment algorithms place arbitrarily with respect to the rest of the network. This can lead to misinterpretations due to the proximity of otherwise unrelated elements. RESULTS We propose a new network layout optimization technique called FragViz which can incorporate additional information on relations between unconnected network components. It uses a two-step approach by first arranging the nodes within each of the components and then placing the components so that their proximity in the network corresponds to their relatedness. In the experimental study with the leukemia gene networks we demonstrate that FragViz can obtain network layouts which are more interpretable and hold additional information that could not be exposed using classical network layout optimization algorithms. CONCLUSIONS Network visualization relies on computational techniques for proper placement of objects under consideration. These algorithms need to be fast so that they can be incorporated in responsive interfaces required by the explorative data analysis environments. Our layout optimization technique FragViz meets these requirements and specifically addresses the visualization of fragmented networks, for which standard algorithms do not consider similarities between unconnected components. The experiments confirmed the claims on speed and accuracy of the proposed solution