W. Pillewizer

Bio: W. Pillewizer is an academic researcher. The author has contributed to research in topics: Cartographic generalization & Scale (map). The author has an hindex of 1, co-authored 1 publications receiving 253 citations.

The Principles of Selection

Abstract: The fundamental of cartographic generalisation, the reduction of the amount of information which can be shown on a map in relation to reduction in scale, is examined. The Principle of Selection was first proposed by Topfer in 1961, and is expressed as an equation relating the number of occurrences of a particular feature at source map scale and at derived map scale. The application of this to small-scale maps involves the introduction of two constants to represent symbolic exaggeration and symbolic form. Examples are given and illustrated. The paper is accompanied by explanatory notes by Dr D. H. Maling, who presented the paper on behalf of the authors at the XX International Geographical Congress London 1964, Section IX, Cartography.

Spatialization Methods: A Cartographic Research Agenda for Non-geographic Information Visualization

Abstract: Information visualization is an interdisciplinary research area in which cartographic efforts have mostly addressed the handling of geographic information. Some cartographers have recently become involved in attempts to extend geographic principles and cartographic techniques to the visualization of non-geographic information. This paper reports on current progress and future opportunities in this emerging research field commonly known as spatialization. The discussion is mainly devoted to the computational techniques that turn high-dimensional data into visualizations via processes of projection and transformation. It is argued that cartographically informed engagement of computationally intensive techniques can help to provide richer and less opaque information visu- alizations. The discussion of spatialization methods is linked to another priority area of cartographic involvement, the development of theory and principles for cognitively plausible spatialization. The paper distinguishes two equally important sets of challenges for cartographic success in spatialization research. One is the recognition that there are distinct advantages to applying a cartographic perspective in information visualization. This requires our community to more thoroughly understand the essence of cartographic activity and to explore the implications of its metaphoric transfer to non-geographic domains. Another challenge lies in cartographers becoming a more integral part of the information visualization community and actively engaging its constituent research fields.

Cartographic generalization in a digital environment: when and how to generalize

Abstract: A key aspect of the mapping process cartographic generalization plays a vital role in assessing the overall utility of both computer-assisted map production systems and geographic information systems. Within the digital environment, a significant, if not the dominant, control on the graphic output is the role and effect of cartographic generalization. Unfortunately, there exists a paucity of research that addresses digital generalization in a holistic manner, looking at the interrelationships between the conditions that indicate a need for its application, the objectives or goals of the process, as well as the specific spatial and attribute transformations required to effect the changes. Given the necessary conditions for generalization in the digital domain, the display of both vector and raster data is, in part, a direct result of the application of such transformations, of their interactions between one another, and of the specific tolerances required. How then should cartographic generalization be embodied in a digital environment? This paper will address that question by presenting a logical framework of the digital generalization process which includes: a consideration of the intrinsic objectives of why we generalize; an assessment of the situations which indicate when to generalize; and an understanding of how to generalize using spatial and attribute transformations. In a recent publication, the authors examined the first of these three components. This paper focuses on the latter two areas: to examine the underlying conditions or situations when we need to generalize, and the spatial and attribute transformations that are employed to effect the changes.

Systematic Generalization, Historical Fate, and the Species Problem

Abstract: The species problem is one of the oldest controversies in natural history. Its persistence suggests that it is something more than a problem of fact or definition. Considerable light is shed on the species problem when it is viewed as a problem in the representation of the natural system (sensu Griffiths, 1974, Acta Biotheor. 23: 85-131; de Queiroz, 1998, Philos. Sci. 55: 238-259). Just as maps are representations of the earth, and are subject to what is called cartographic generalization, so diagrams of the natural system (evolutionary trees) are representations of the evolutionary chronicle, and are subject to a temporal version of cartographic generalization which may be termed systematic generalization. Cartographic generalization is based on judgements of geographical importance, and systematic generalization is based on judgements of historical importance, judgements expressed in narrative sentences (sensu Danto, 1985, Narration and knowledge, Columbia Univ. Press, New York). At higher systematic levels these narrative sentences are conventional and retrospective, but near the “species” level they become prospective, that is, dependent upon expectations of the future. The truth of prospective narrative sentences is logically indeterminable in the present, and since all the common species concepts depend upon prospective narration, it is impossible for any of them to be applied with precision.

Multiscale visualization using data cubes

Abstract: Most analysts start with an overview of the data before gradually refining their view to be more focused and detailed. Multiscale pan-and-zoom systems are effective because they directly support this approach. However, generating abstract overviews of large data sets is difficult and most systems take advantage of only one type of abstraction: visual abstraction. Furthermore, these existing systems limit the analyst to a single zooming path on their data and thus to a single set of abstract views. This paper presents: 1) a formalism for describing multiscale visualizations of data cubes with both data and visual abstraction and 2) a method for independently zooming along one or more dimensions by traversing a zoom graph with nodes at different levels of detail. As an example of how to design multiscale visualizations using our system, we describe four design patterns using our formalism. These design patterns show the effectiveness of multiscale visualization of general relational databases.

Process mining in flexible environments

Abstract: ion Atoms >> manifested-as >> >> observed-as >>