Stefan Steiniger

An overview on current free and open source desktop GIS developments

Over 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.

The 2012 free and open source GIS software map - A guide to facilitate research, development, and adoption

Over the last decade an increasing number of free and open source software projects have been founded that concentrate on developing several types of software for geographic data collection, storage, analysis and visualization. We first identify the drivers of such software projects and identify different types of geographic information software, e.g. desktop GIS, remote sensing software, server GIS etc. We then list the major projects for each software category. Afterwards we discuss the points that should be considered if free and open source software is to be selected for use in business and research, such as software functionality, license types and their restrictions, developer and user community characteristics, etc. Finally possible future developments are addressed.

Free and Open Source Geographic Information Tools for Landscape Ecology

Abstract Geographic Information tools (GI tools) have become an essential component of research in landscape ecology. In this article we review the use of GIS (Geographic Information Systems) and GI tools in landscape ecology, with an emphasis on free and open source software (FOSS) projects. Specifically, we introduce the background and terms related to the free and open source software movement, then compare eight FOSS desktop GIS with proprietary GIS to analyse their utility for landscape ecology research. We also provide a summary of related landscape analysis FOSS applications, and extensions. Our results indicate that (i) all eight GIS provide the basic GIS functionality needed in landscape ecology, (ii) they all facilitate customisation, and (iii) they all provide good support via forums and email lists. Drawbacks that have been identified are related to the fact that most projects are relatively young. This currently affects the size of their user and developer communities, and their ability to include advanced spatial analysis functions and up-to-date documentation. However, we expect these drawbacks to be addressed over time, as systems mature. In general, we see great potential for the use of free and open source desktop GIS in landscape ecology research and advocate concentrated efforts by the landscape ecology community towards a common, customisable and free research platform.

Free and Open Source GIS Software for Building a Spatial Data Infrastructure

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.

An Approach for the Classification of Urban Building Structures Based on Discriminant Analysis Techniques

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.

Relations among Map Objects in Cartographic Generalization

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 of map object relations. Five different types of horizontal relations are identified: geometric, topological, semantic, statistical and structural. Some of these can be based on standard operations available in commercial GIS or mapping systems, while others are less easily accessible. To demonstrate the use of our typology and show how complex horizontal relations can be formalized, we present an application of the typology to the grouping and generalization of islands. Subsequently, we discuss the various steps involved in the usage of horizontal relations in map generalization, as well as their associated roles.

Utilising urban context recognition and machine learning to improve the generalisation of buildings

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.

PlanYourPlace – A geospatial infrastructure for sustainable community planning

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

Recognition of island structures for map generalization

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.

Modelling Geographic Relationships in Automated Environments

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.