The Potential of Participedia as a Crowdsourcing Tool for Comparative Analysis of Democratic Innovations

For two reasons, our capacity for systematic comparison of innovative participatory democratic processes remains limited. First, the category of participatory democratic innovations remains relatively vague when compared to more traditional democratic institutions and practices. Second, until recently there existed no large-sample databases that captured relevant variables in the practice of democratic innovation. The lone exception to these patterns is the Participedia database, located online. Participedia is well placed to respond to the two obstacles to systematic comparative research on democratic innovation. First, its crowdsourced data collection strategy means that many of the cases on the platform are not well known and have not been the subject of sustained academic analysis. Second, the data captured in the articles provides the basis for systematic comparative analysis of democratic innovations both within type (e.g., participatory budgeting, mini-publics) and across types. The platform allows for systematic content analysis of text descriptions and/or statistical analysis of the datasets generated from the structured data fields. This article describes the data about innovative participatory democratic processes available from Participedia, and furnishes examples of the kinds of quantitative and qualitative insights about those processes that Participedia enables.

The LH5 model for data mining

In the age of E-Business many companies faced with massive data sets that must be analysed for gaining a competitive edge. these data sets are in many instances incomplete and quite often not of very high quality. Although statistical analysis can be used to pre-process these data sets, this technique has its own limitations. In this paper we are presenting a system - and its underlying model - that can be used to test the integrity of existing data and pre-process the data into clearer data sets to be mined. LH5 is a rule-based system, capable of self-learning and is illustrated using a medical data set.

Wind resource mapping using landscape roughness and spatial interpolation methods

Energy saving, reduction of greenhouse gasses and increased use of renewables are key policies to achieve the European 2020 targets. In particular, distributed renewable energy sources, integrated with spatial planning, require novel methods to optimise supply and demand. In contrast with large scale wind turbines, small and medium wind turbines (SMWTs) have a less extensive impact on the use of space and the power system, nevertheless, a significant spatial footprint is still present and the need for good spatial planning is a necessity. To optimise the location of SMWTs, detailed knowledge of the spatial distribution of the average wind speed is essential, hence, in this article, wind measurements and roughness maps were used to create a reliable annual mean wind speed map of Flanders at 10 m above the Earth’s surface. Via roughness transformation, the surface wind speed measurements were converted into meso- and macroscale wind data. The data were further processed by using seven different spatial interpolation methods in order to develop regional wind resource maps. Based on statistical analysis, it was found that the transformation into mesoscale wind, in combination with Simple Kriging, was the most adequate method to create reliable maps for decision-making on optimal production sites for SMWTs in Flanders.