Academic journal article Cartography and Geographic Information Science

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

Academic journal article Cartography and Geographic Information Science

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

Article excerpt

Introduction

A number of principal approaches have been put forward during recent years o give people the means for making sense of large, complex, and often unstructured data repositories. The problems encountered are shared across many knowledge domains. This has led to the development of distinct cross-disciplinary approaches, which draw on the accumulated knowledge of different academic traditions. For example, it would be hard to discuss current data mining efforts without considering the role of traditional statistical inference. Likewise, one cannot ignore the influence of the vector-space model (Salton 1968) on modern knowledge discovery tools. It is surprising then that while mapping metaphors have long been popular in information visualization, decades of cartographic research--not to mention the broader cartographic tradition--have often been all but ignored (Card et al. 1999). Arguably, cartographers and geographers should be faulted more than anyone else for failing to engage the interdisciplinary information visualization community by demonstrating the relevance of their accumulated expertise. While computer scientists have been the most active contributors to information visualization research, and the institutional infrastructure is dominated by IEEE and ACM (1) activities, information visualization has remained an open, inclusive, interdisciplinary research activity.

Among cartographic research into non-geographic information visualization one can distinguish two strands of activities. Some cartographers are engaged in the interpretation and transformation of specific computational approaches in the light of cartographic tradition and informed by geographic information science (Skupin 2000, 2002a; Skupin and Buttenfield 1996). In contrast to this computational perspective, the cognitive approach emphasizes the user side of spatialization. It aims at providing visualizations that function in accordance with what we know, or would like to know, about human perception and cognition of geographic space and its visual representations (Fabrikant 2001a; Fabrikant and Buttenfield 2001). The two perspectives are complementary, with geographic information science providing for a synthesis that matches geometric primitives against the cognitive categories that underlie our understanding and representation of space (Couclelis 1998; Fabrikant and Buttenfield 2001; Skupin 2002b).

Some of the influences behind cartographic spatialization are distinctly geographic; others are shared with a number of fields, such as cognitive science, linguistics, information science, and human-computer interaction (HCI). Within geographic information science, spatialization is most closely associated with the geographic visualization rubric (Buckley et al. 2000), but it also shares some common interests and methods with geographic data mining and knowledge discovery (Miller and Han 2001). Among specific geographic influences, the First Law of Geography (Tobler 1970) is particularly noteworthy. It boils down to the observation that everything is related to everything else, but closer things are more closely related than distant things. This principle played a role in the choice of multidimensional scaling for text visualization (Skupin and Buttenfield 1996), and it inspired ongoing efforts to uncover the cognitive underpinnings of spatialization (Fabrikant et al. 2002). Apart from visual depictions that one would now rightfully call spatializations (Goodchild and Janelle 1988; Li 1998) there are related efforts by geographers dealing with the mapping of cyberspace (Dodge and Kitchin 2001), investigation of specific methods commonly used for spatialization (Lloyd 2000), and use of spatialization as an alternative tool for analyzing human subject tests (Mark et al. 2001).

This paper gives an overview of a number of issues relevant to a successful engagement of information visualization by cartographers. …

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