André Skupin

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

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 visualizations. 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.

Visualizing Demographic Trajectories with Self-Organizing Maps

In recent years, the proliferation of multi-temporal census data products and the increased capabilities of geospatial analysis and visualization techniques have encouraged longitudinal analyses of socioeconomic census data. Traditional cartographic methods for illustrating socioeconomic change tend to rely either on comparison of multiple temporal snapshots or on explicit representation of the magnitude of change occurring between different time periods. This paper proposes to add another perspective to the visualization of temporal change, by linking multi-temporal observations to a geometric configuration that is not based on geographic space, but on a spatialized representation of n-dimensional attribute space. The presented methodology aims at providing a cognitively plausible representation of changes occurring inside census areas by representing their attribute space trajectories as line features traversing a two-dimensional display space. First, the self-organizing map (SOM) method is used to transform n-dimensional data such that the resulting two-dimensional configuration can be represented with standard GIS data structures. Then, individual census observations are mapped onto the neural network and linked as temporal vertices to represent attribute space trajectories as directed graphs. This method is demonstrated for a data set containing 254 counties and 32 demographic variables. Various transformations and visual results are presented and discussed in the paper, from the visualization of individual component planes and trajectory clusters to the mapping of different attributes onto temporal trajectories.

Features, Objects, and Other Things: Ontological Distinctions in the Geographic Domain

Two hundred and sixty-three subjects each gave examples for one of five geographic categories: geographic features, geographic objects, geographic concepts, something geographic, and something that could be portrayed on a map. The frequencies of various responses were significantly different, indicating that the basic ontological terms feature, object, etc., are not interchangeable but carry different meanings when combined with adjectives indicating geographic or mappable. For all of the test phrases involving geographic, responses were predominantly natural features such as mountain, river, lake, ocean, hill. Artificial geographic features such as town and city were listed hardly at all for geographic categories, an outcome that contrasts sharply with the disciplinary self-understanding of academic geography. However, geographic artifacts and fiat objects, such as roads, cities, boundaries, countries, and states, were frequently listed by the subjects responding to the phrase something that could be portrayed on a map. In this paper, we present the results of these experiments in visual form, and provide interpretations and implications for further research.

Chapter 35 – Cognitively Plausible Information Visualization

This chapter proposes a framework for the construction of cognitively plausible semantic information spaces and emphasizes on the ways in which the framework may be applied towards the design of cognitively adequate spatializations. A cognitively plausible Information Visualization is designed such that it matches humans internal visualization capabilities. The proposed framework focuses on the use of geographic space as a data generalization strategy (ontology) and the use of spatial representations or maps to depict these data abstractions. The building blocks of this spatialization framework are informed by geographic information theory and include principles of ontological modeling such as semantic generalization (spatial primitives), geometric generalization (visual variables), association (source–target domain mapping through spatial metaphors), and aggregation (hierarchical organization). Spatialization is defined as a data transformation method based on spatial metaphors, with the aim of generating a cognitively adequate graphic representation for data exploration and knowledge discovery in multi-dimensional databases. Spatialization not only provides the construction of visual descriptions and summaries of large data repositories but also creates opportunities for visual queries and sense-making approaches.

On Geometry and Transformation in Map-Like Information Visualization

A number of visualization techniques have been put forward that implement a map metaphor to display abstract, non-georeferenced information. This paper refers to these as map-like information visualizations that are distinguished from other information visualization approaches in a number of ways. It interprets some of the principles underlying these techniques within a framework informed by geographic information science (GIScience). Recent geographic efforts in this research area have linked ideas about the nature of geographic information to cognitive schemata proposed by cognitive linguists. This paper draws on the arguments that have emerged from those efforts regarding the nature and usefulness of geographic metaphors. It proposes to discuss particular projection techniques, like multidimensional scaling or self-organizing maps, with reference to the geometric primitives they employ. These primitives will drive the choice of geometric and symbolic transformations that are necessary to achieve a particular visualization. Designers of map-like visualizations are thus challenged to seriously consider the implications of particular computational techniques and the consequences of symbolization choices.

Attribute space visualization of demographic change

This paper introduces an approach for closer integration of self-organizing maps into the visualization of spatio-temporal phenomena in GIS. It is proposed to provide a more explicit representation of changes occurring inside socio-economic units by representing their attribute space trajectories as line features traversing a two-dimensional display space. A self-organizing map consisting of several thousand neurons is first used to create a high-resolution representation of attribute space in two dimensions. Then, multi-year observations are mapped onto the neural network and linked to form trajectories. This method is implemented for a data set containing 254 counties and 34 demographic variables. Various visual results are presented and discussed in the paper, from the visualizations of individual component planes to the mapping of voting behavior onto temporal trajectories.