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International audienceOver the past few years the world of free and open source geospatial software has experienced some major changes. For instance, the website FreeGIS.org currently lists 330 GIS‐related projects. Besides the advent of new software projects and the growth of established projects, a new organisation known as the OSGeo Foundation has been established to offer a point of contact. This paper will give an overview on existing free and open source desktop GIS projects. To further the understanding of the open source software development, we give a brief explanation of associated terms and introduce the two most established software license types: the General Public License (GPL) and the Lesser General Public License (LGPL). After laying out the organisational structures, we describe the different desktop GIS software projects in terms of their main characteristics. Two main tables summarise information on the projects and functionality of the currently available software versions. Finally, the advantages and disadvantages of open source software, with an emphasis on research and teaching, are discussed

Recognition of urban structures is of interest in cartography and urban modelling. While a broad range of typologies of urban patterns have been published in the last century, relatively little research on the automated recognition of such structures exists. This work presents a sample-based approach for the recognition of five types of urban structures: (1) inner city areas, (2) industrial and commercial areas, (3) urban areas, (4) suburban areas and (5) rural areas. The classification approach is based only on the characterisation of building geometries with morphological measures derived from perceptual principles of Gestalt psychology. Thereby, size, shape and density of buildings are evaluated. After defining the research questions we develop the classification methodology and evaluate the approach with respect to several aspects. The experiments focus on the impact of different classification algorithms, correlations and contributions of measures, parameterisation of buffer-based indices, and mode filtering. In addition to that, we investigate the influence of scale and regional factors. The results show that the chosen approach is generally successful. It turns out that scale, algorithm parameterisation, and regional heterogeneity of building structures substantially influence the classification performance.

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Over the past 20 years a set of methods for home-range estimation and analysis of animal observation data have been developed. Whereas comparisons among the estimation methods and different estimation software are available, only the adehabitat analysis toolbox for R is under a free and open-source software license and includes established and new home-range estimation approaches, such as Kernel Density Estimation, Brownian Bridges, and Local Convex Hulls. However, R and adehabitat are command line based, which some may perceive as not very user-friendly, and provide only a limited set of functions for the analysis of home ranges with environmental geospatial data (e.g., land cover and elevation data). This article presents a free and open-source home-range analysis toolbox that focuses on the evaluation of global positioning system collar data, and integrates with a desktop geographic information system to allow data analysis beyond the creation of home ranges. The software is distributed under a free and open-source license, so research can also benefit from the toolbox because implemented algorithms can be tested directly and improved. ß 2012 The Wildlife Society.KEY WORDS GIS, home range analysis, kernel density estimation, movement analysis, open-source software.