Abstracting Geographic Information in a Data Rich World

It would be a mistake to see map generalisation merely as the automation of a set of cartographic practices. The process of representing various geographies at different levels of detail goes to the heart of geographical understanding. Comprehension and context comes from being able to examine information at multiple levels of detail. An automated environment that can support such interaction depends upon a rich understanding of the qualities, behaviours and relationships among the various geographic phenomena that are being mapped. In this chapter we seek to explore the complexity of map generalisation, reflecting on the impact of the changing ways in which we gather and interact with geographic information. This in turn provides a justification for the structure of the book which is then summarised in the second half of this chapter.

In traditional generalisation flow lines, the target map is specified upstream, manually, by cartographers and is intended to answer generic, well-identified user needs. In the emerging context of on-demand mapping, maps have to be derived automatically for users whose requirements are not known in advance, and who may need to integrate their own data. The definition of suitable target map specifications thus becomes part of the service, which raises challenges that are explored in this chapter. The first challenge is to set up a formal map specifications model, rich enough to guide the whole map derivation process. The second challenge is to collect requirements and to assist the user, who is not supposed to be a map designer, in the specification of a map usable for their task and one that respects cartographic standards.

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.

Spatial zoom and thematic navigation are indispensable functionalities for digital web and mobile maps. Therefore, recent map generalisation research has introduced the first truly smooth vario-scale structure (after several near vario-scale representations), which supports continuous or smooth zooming. In the implementation, the vario-scale representation of 2D geo-information can be stored as a single 3D (2D+scale) data structure. A single uniform scale map in 2D is then derived by computing a horizontal slice through the structure.

The availability of spatial data on the web has greatly increased through the availability of user-generated community data and geosensor networks. The integration of such multi-source data is providing promising opportunities, as integrated information is richer than can be found in only one data source, but also poses new challenges due to the heterogeneity of the data, the differences in quality and in respect of tag-based semantic modelling. The chapter describes approaches for the integration of official and informal sources, and discusses the impact of integrating user-generated data on automated generalisation and visualisation.

This chapter summarises cartographic generalisation operators used to generalise geospatial data. It includes a review of recent approaches that have been tested or implemented to generalise networks, points, or groups. Emphasis is placed on recent advances that permit additional flexibility to tailor generalisation processing in particular geographic contexts, and to permit more advanced types of reasoning about spatial conflicts, preservation of specific feature characteristics, and local variations in geometry, content and enriched attribution. Rather than an exhaustive review of generalisation operators, the chapter devotes more attention to operators associated with network generalisation, which illustrates well the logic behind map generalisation developments. Three case studies demonstrate the application of operators to road thinning, to river network and braid pruning, and to hierarchical point elimination. The chapter closes with some summary comments and future directions.

Process modelling has always been an important part of research in generalisation. In the early days this would take the form of a static sequence of generalisation actions, but currently the focus is on modelling much more complex processes, capable of generalising geographic data into various maps according to specific user requirements. To channel the growing complexity of the processes required, better process models had to be developed. This chapter discusses several aspects of the problem of building such systems. As the system gets more complex, it becomes important to be able to reuse components which already exist. Web services have been used to encapsulate generalisation processes in a way that maximises their interoperability and therefore reusability. However, for a system to discover and trigger such a service, it needs to be formalised and described in a machine understandable way, and the system needs to have the knowledge about where and when to use such tools. This chapter therefore explores the requirements and potential approaches to the design and building of such systems.

This chapter reviews recent development in terrain generalisation. We focus on issues of aesthetics and legibility in the application of cartographic generalisation. Generalisation methods are relevant to traditional terrain representations (spot heights, contours, hypsometric colours, shaded relief) and to grid and triangulated surface generalisation. First we consider issues related to relief representation at different scales. As generalisation requires knowledge about the terrain morphology, several approaches focusing on the classification of terrain features according to morphometric or topological criteria have been developed. Cartographic generalisation methods are reviewed with consideration given to conflicts between terrain representations and other object type data on the map. In the second part of this chapter, three case studies illustrating previous developments are presented. First, a generalisation method for hypsometric map production is described where important valleys and mountain ridges are accentuated to improve their representation. Second, a method selecting features represented by isobaths and answering specific constraints of nautical charts is presented. The third case study is a generalisation method which models the relationship between terrain and other objects such as buildings and rivers.

This chapter presents the context, the issues and the research associated with the evaluation of map generalisation output as well as of map readability. Two main approaches of evaluation are described, i.e. visual and quantitative evaluation. Visual evaluation is subjective, qualitative, and time-consuming, while it is argued that quantitative evaluation is only appropriate for assessing specific aspects. Since automated evaluation is becoming very important in the field of automated generalisation, this chapter further explores the topic of automated evaluation. The previous frameworks for automated generalisation are reviewed and the three main components of automated evaluation are explained. Related to automated evaluation of generalisation output are formulas to automatically evaluate map readability. These are also discussed. This chapter ends with three case studies. The first Case study identifies and evaluates generalised building patterns. It demonstrates the three-step approach of data enrichment, data matching and constraint evaluation. The second Case study deals with formulas to automatically evaluate map readability and the third Case study carries out a comprehensive evaluation demonstrating the main aspects described in this chapter. Both visual and quantitative evaluation are applied of which the last one includes the three main components of automated evaluation. The chapter closes with conclusions and highlights research issues in evaluation.

In the last decade schematised maps have garnered substantial research interest from disciplines such as cartography, computational geometry and spatial cognition. More often than not, the approaches have been following their own specific goals, leaving the question of what they have in common relatively open. Most research has had metro maps and their automated creation as its focus. In this chapter we seek a more systematic treatment of what constitutes schematised maps. This chapter organises and differentiates the understanding of what schematisation is and how it relates to generalisation. Three cases studies variously explore and illustrate developments in the automatic generation of schematised maps.

National Mapping Agencies (NMAs) are still among the main end users of research into automated generalisation, which is transferred into their production lines via various means. This chapter includes contributions from seven NMAs, illustrating how automated generalisation is used in practice within their partly or fully automated databases and maps production lines, what results are currently being obtained and what further developments are on-going or planned. A contribution by the European Joint Research Center reports on the use of multiple representation and generalisation in the context of the implementation of the European INSPIRE directive. The chapter finishes with a synthesis of recent achievements, as well as future challenges that NMAs have begun to tackle.

The core ambitions of map generalisation remain the same-as do a set of inter connected research activities concerned with algorithm developments, user requirements modelling, evaluation methodologies, and the handling and integration of multi scale, global datasets. Technology continues to afford new paradigms of data capture and use, in turn requiring generalisation techniques that are capable of working with a wider variety of data sources (including user generated content), and web friendly mapping services that conceal the complexities of the map design process from the lay user. This summarative chapter highlights yet again, the truly inter disciplinary nature of map generalisation research.