Nicolas Regnauld

A synoptic View of Generalisation Operators

Publisher Summary This chapter provides an overview of what has been achieved so far toward creating a comprehensive set of generalization operators. It contains discussions related to the classification of these operators, and how different classifications have been defined to suite different contexts; it proposes a generic list of generalization operators and a detailed list of implementation of these operators for different types of features. This chapter discusses a virtual toolbox that can be used when designing automatic generalization solutions. Most of the research in generalization assumes that the process can be broken down into a series of logical operations that can be classified according to the type of geometry of the feature, into generalization operators. For instance, a smoothing operator is designed for linear features, while an amalgamation operator works on areal features. Advances in hardware and modeling tools provide new opportunities to develop new types of algorithms. The list of different types of phenomena that need to be processed during the generalization is very large, and the list of representations that one may want to derive for each of them is larger still. Thus, there is always a need to develop new algorithms, particularly those that can take into account the context in which they operate. The chapter concludes by discussing the changing nature of algorithms and operators in response to technological developments and changing contexts of use.

Methodology for evaluating automated map generalization in commercial software

This paper presents a methodology developed for a study to evaluate the state of the art of automated map generalization in commercial software without applying any customization. The objectives of this study are to learn more about generic and specific requirements for automated map generalization, to show possibilities and limitations of commercial generalization software, and to identify areas for further research. The methodology had to consider all types of heterogeneity to guarantee independent testing and evaluation of available generalization solutions. The paper presents the two main steps of the methodology. The first step is the analysis of map requirements for automated generalization, which consisted of sourcing representative test cases, defining map specifications in generalization constraints, harmonizing constraints across the test cases, and analyzing the types of constraints that were defined. The second step of the methodology is the evaluation of generalized outputs. In this step, three evaluation methods were integrated to balance between human and machine evaluation and to expose possible inconsistencies. In the discussion the applied methodology is evaluated and areas for further research are identified.

A functional perspective on map generalisation

In the context of map generalisation, the ambition is to store once and then maintain a very detailed geographic database. Using a mix of modelling and cartographic generalisation techniques, the intention is to derive map products at varying levels of detail – from the fine scale to the highly synoptic. We argue that in modelling this process, it is highly advantageous to take a ‘functional perspective’ on map generalisation – rather than a geometric one. In other words to model the function as it manifests itself in the shapes and patterns of distribution of the phenomena being mapped – whether it be hospitals, airports, or cities. By modelling the functional composition of such features we can create relationships (partonomic, taxonomic and topological) that lend themselves directly to modelling, to analysis and most importantly to the process of generalisation. Borrowing from ideas in robotic vision this paper presents an approach for the automatic identification of functional sites (a collection of topographic features that perform a collective function) and demonstrates their utility in multi-scale representation and generalisation.

Generalisation in Practice Within National Mapping Agencies

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.

Generalisation log for managing and utilising a multi-representation spatial database in map production

This paper describes a data model for logging map generalisation process information to facilitate data authentication and update propagation in a multi-representation spatial database environment. Generalisation is modelled as a directed acyclic diagram of prioritised generalisation process instances, which is further divided into independent operations as the atomic functional unit for generalisation. Generalisation parameter values and the roles of features in operations are stored. The full generalisation history of each feature may be traced for data authentication and quality analysis, or for update propagation. This model also includes the design of a feature versioning mechanism. In addition, OSGenLog, a set of platform-independent APIs to create generalisation logs, has been implemented in Gothic object-oriented database management system.

Map Specifications and User Requirements

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.

Process Modelling, Web Services and Geoprocessing

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.

Experiments to Distribute and Parallelize Map Generalization Processes

Automatic map generalization requires the use of computationally intensive processes often unable to deal with large datasets. Distributing the generalization process is the only way to make them scalable and usable in practice. But map generalization is a highly contextual process, and the surroundings of a generalized map feature needs to be known to generalize the feature, which is a problem as distribution might partition the dataset and parallelize the processing of each part. This paper proposes experiments to evaluate the past propositions to distribute map generalization, and to identify the main remaining issues. The past propositions to distribute map generalization are first discussed, and then the experiment hypotheses and apparatus are described. The experiments confirmed that regular partitioning was the quickest strategy, but less effective when taking context into account. The geographical partitioning, though less effective for now, is quite promising regarding the quality of the results as it better integrates the geographical context.