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PDF papers
id ddssup0213
authors Osaragi, Toshihiro
year 2002
title Classification Methods for Spatial Data Representation
source Timmermans, Harry (Ed.), Sixth Design and Decision Support Systems in Architecture and Urban Planning - Part two: Urban Planning Proceedings Avegoor, the Netherlands), 2002
summary In the process of representing quantitative spatial data on a map, it is necessary to classify attribute values into some class divisions. When a number of classes are employed, the characteristics of spatial distribution of original data can be expressed faithfully. However, its legends might become rather complicated and the delicate color differences in the represented map would be difficult to distinguish. On the other hand, when employing a few classes, the information such as small vibrating factors or local peaks might be ignored; namely, much information of original data will be lost. Hence, we should discuss how many classes are necessary to represent spatial data. Furthermore, even if the same spatial data are represented using the same number of classes, we might obtain the quite different maps according to the choice of classification methods incorporated in existing geographic information systems. Namely, the characteristics of the original data might be overlooked, or there might be a risk of mistaking judgment, if we do not have enough knowledge about classification methods as well as the nature of original data. Hence, we should also discuss how the boundary value between each class should be set. In this paper, a new classification method using an evaluation function based on Akaike’s Information Criterion is proposed, and is applied to actual spatial data. Next, based on the consideration about its result, another classification method minimizing information loss of original data is proposed. Furthermore, numerical examples of its applications are achieved through the comparison with existing classification methods.
series DDSS
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