Cognitive Issues in Geographic Information Visualization

Perception and Thematic Map Symbol Design I

3708.3 - The Method of Minimum Information Loss as a Classification Method for Visualizing Spatial Data

Monday, July 3
4:50 PM - 5:10 PM
Location: Maryland C

When visualizing spatial data for which attribute values are quantitatively defined, there is always a need for classification. More specifically, the general method is to classify data within a certain range into the same class, and indicate it with the same color. Existing geographic Information systems are equipped with a number of different methods for automatically performing classification, but the displayed thematic maps vary greatly in appearance depending on the method used. Existing classification methods should be used selectively to suit the purpose of analysis or the properties of the spatial data to be displayed, but at the early stage of analysis, or for general end users, it is necessary to have a method which is simpler, applicable to any type of spatial data, and enables display of the properties of the data without bias. This paper proposes a spatial data classification method based on the minimization of information loss and compare the results with five other classification methods. Each method is applied to seven different sets of actual spatial data, and they are classified into nine classes. Then, the ratio L of information loss by each method is compared with other methods. The results of numerical analysis demonstrate that the classification method based on the minimization of information loss can flexibly cope with all kinds of spatial data, and is particularly effective at the initial stage of analysis, or for browsing of spatial data by an ordinary end user.

Toshihiro Osaragi

Professor
Tokyo Institute of Technology

Prof. Osaragi's areas of specialization include a wide range of cross-disciplinary fields. His research interest includes the spatial analysis of urban activities, the mathematical modeling of spatial choice, spatial cognition, the integration of spatial analysis and GIS, spatial statistics, exploratory spatial data analysis, the development of decision support systems, planning theory of housing estates and its renewal, urban disaster prevention planning.

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Amy L. Griffin

Senior Lecturer
UNSW Canberra

Dr. Amy Griffin is a Senior Lecturer in Geography at UNSW Canberra. She is currently a co-chair of the ICA Commission on Cognitive Issues in Geographic Information Visualization (CogVIS). Her research interests include investigating cognitive and perceptual processes involved in using maps, information visualizations and other forms of geospatial information.

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