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The demand for geospatial data across different disciplines and organisations has led to the development and implementation of spatial data infrastructures (SDI) and the theory and concepts behind them. An SDI is an evolving concept about facilitating and coordinating the exchange of geospatial data and services between stakeholders from different levels in the spatial data community. Universities and other research organisations typically have well-established libraries and digital catalogues for scientific literature, but catalogues for geospatial data are rare. Geospatial data is widely used in research, but geospatial data produced by researchers is seldom available, accessible and usable, e.g., for purposes of teaching or further research after completion of the project. This chapter describes the experiences of a number of SDI implementations at universities and research institutes. Based on this, the Academic SDI, an SDI for research and education, is defined and its stakeholders are described. The purpose, scope and stakeholders of the Academic SDI are described based on the formal model of an SDI developed by the International Cartographic Association (ICA) Commission on SDIs and Standards (formerly the Commission on Geoinformation Infrastructures and Standards). The results contribute to understanding the state-of-the-art in SDI implementations at universities and research institutes; how the Academic SDI differs from a ‘regular’ SDI; and which role players need to be involved in a successful SDI implementation for research and education.

Over the past twenty 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 adahabitat 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 rather catering the needs of the scientific user and not the wildlife manager with respect to its general look & feel, and further, provide only a limited set of functions for the analysis of home ranges with environmental geodata, e.g. land-cover and elevation data. This article presents a free and open-source home range analysis toolbox that (i) focuses on the evaluation of GPS collar data, (ii) aims to be fairly straight forward to use by the wildlife manager, and (iii) integrates with a desktop GIS to allow data analysis beyond the creation of home ranges. Since the developed software is distributed under a free and open source license also research can benefit since implemented algorithms can be directly tested and improved.

Technological advances over the past 5 to 10 years have made Geographic Information Systems a powerful and affordable tool for geographic analysis and urban planning. These technological advances have also enabled and shaped new forms of communication and participation, particularly within the domain of social networking via webpages such as Facebook, Twitter, and LinkedIn. Connecting the analytical power of GIS with mapping tools and interaction capabilities of web 2.0 technologies, as well as with environmental, economic, and social models should result in a promising toolbox for urban planning. This article presents a framework that outlines requirements and constraints for a web-accessible planning platform within the context of sustainable urban development of established neighbourhoods in the City of Calgary, Canada. The platform focuses not only on the urban planner as user, but more specifically on the citizen as a contributor to the planning and development process, to further include public opinion in the planning process. The following three aspects for the implementation of the participatory planning platform are

International audience; Utilising urban context recognition and machine learning to improve the generalisation of buildings Abstract The introduction of automated generalisation procedures in map production systems requires that generalisation systems are capable of processing large amounts of map data in acceptable time and that cartographic quality is similar to traditional map products. With respect to these requirements, we examine two complementary approaches that should improve generalisation systems currently in use by national topographic mapping agencies. Our focus is particularly on self-evaluating systems, taking as an example those systems that build on the multi-agent paradigm. The first approach aims to improve the cartographic quality by utilising cartographic expert knowledge relating to spatial context. More specifically, we introduce expert rules for the selection of generalisation operations based on a classification of buildings into five urban structure types, including inner city, urban, suburban, rural, and industrial and commercial areas. The second approach aims to utilise machine learning techniques to extract heuristics that allow us to reduce the search space and hence the time in which a good cartographical solution is reached. Both approaches are tested individually and in combination for the generalisation of buildings from map scale 1:5000 to the target map scale of 1:25 000. Our experiments show improvements in terms of efficiency and effectiveness. We provide evidence that both approaches complement each other and that a combination of expert and machine learnt rules give better results than the individual approaches. Both approaches are sufficiently general to be applicable to other forms of self-evaluating, constraint-based systems than multi-agent systems, and to other feature classes than buildings. Problems have been identified resulting from difficulties to formalise cartographic quality by means of constraints for the control of the generalisation process.

International audience; Automated processes such as cartographic generalisation require formal abstraction of the geographic space in order to analyse, process and transform it. Spatial relations are key to understanding geographic space and their modelling is a critical issue. This chapter reports on existing classifications and modelling frameworks for spatial relations. A generic model is proposed for building an ontology of spatial relations for automatic processes such as generalisation or on-demand mapping, with a focus on so-called multiple representation relations. Propositions to use such ontology in an automated environment are reported. The three use cases of the chapter describe recent research that uses relations modelling. The first use case is the extension of CityGML with relations for 3D city models. The second use case presents the use of spatial relations for automatic spatial analysis, and particularly the grouping of natural features such as lakes or islands. Finally, the third use case is a data migration model guided by relations that govern the positioning of thematic data upon changing reference data.

The implementation of Spatial Data Infrastructures (SDIs) for governments and companies is a task that has gained ample attention in recent years. Different categories of spatial software such as desktop GIS, server GIS, web map servers, spatial database management systems, web map development toolkits, etc., are required to realize the software components of an SDI. We catalogue a (selected) variety of free and open source projects that develop and maintain spatial software that fit within these categories. Our analysis reveals that for all categories of software used in SDIs a free software product is available. This enables adopters to implement an SDI on a limited financial budget, and allows the distribution of a proven SDI architecture without legal constraints. Our software evaluation shows that free and open source solutions support a wide range of industry standards that ease interoperability between SDI components.

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.

Adequate representation of cartographic expert knowledge is essential if maps are to be created in an automated way. Part of this expert knowledge is made up by the structural knowledge embedded in the relations that exist among the objects depicted on a map, as these define the structures and patterns of the corresponding real-world objects that should be maintained and emphasized in the cartographic generalization process. With this article we aim to provide a foundation for the analysis and representation of such relations among objects in thematic and topographic maps, which we term horizontal relations. We start off by defining the terminology underlying map object relations and by discussing how these relations interact with map constraints and cartometric measures. We then present a typology of horizontal relations that may be found in a map with respect to map generalization. The typology is the result of a study of thematic and topographic maps, as well as an analysis of the literature on the use...

In this paper we describe work on the automatic recognition of island structures. In an initial phase several test persons were asked to mark groups of islands that they perceived on test maps. Based on these experimental results the island structures were categorized with respect to size and shape, and their construction described using principles from Gestalt theory. Based on those descriptions of island structures we will present an algorithm for the detection of large groups of islands based on a Minimal Spanning Tree (MST). Therefore, we apply split and merge operations on the MST. For the automated characterization of the shape and orientation of island groups we propose to use principal components obtained from a PCA. The results of the algorithm are then visually compared with the island groups previously marked by test persons and shortcomings of the approach are discussed.