On the Parametric Description of the Shape of the Cartographic Line

Abstract

The description of the nature and shape of the cartographic line has been a challenging research area in contemporary cartography. The digital character of the line, while representing a tempting research element, reveals the extent of the problems related to line character assessment because of the practically unlimited number of forms the cartographic line can take. Recent research has resulted in a number of parameters (measures) describing the line shape qualitatively and quantitatively. This paper deals with the following two themes: (1) the quantitative description of line character through a number of parameters; and (2) the development of a methodology for the segmentation of the cartographic line into "homogeneous" parts. The identification, selection, and clustering of parameters, leading to the sufficient description of line character, is elaborated. The "knowledge" thus acquired is valuable for the objective description of line shape and constitutes the basis for addressing other problems, such as the realistic estimation of positional error in data acquisition and the efficient generalization of linear features.

Hybrid line simplification for cartographic generalization

“…Furthermore, when the parameters are applied, the scale of the simplified result should be the same as that of the other algorithms. As a measurement of the shape characteristics, the geometric measurements of Lopez and Balboa (2008) and Skopeliti and Tsoulos (1999) and many others can be employed. The clustering methods and associated parameters can be chosen according to the needs of the user concerning abstracting the training data for segmentation.…”

“…It is also a shortcoming of these studies that they mainly depend on the subjective knowledge of experts to determine the subsections in a line feature and to identify the appropriate algorithms or parameters. Moreover, the generalization quality is evaluated merely by a visual or qualitative assessment in many cases (Skopeliti and Tsoulos, 1999;Chen and Chen, 2005;Mustiere, 2005;Lopez, 2008, 2009;Lopez and Balboa, 2008).…”

Generalization-oriented Road Line Classification by Means of an Artificial Neural Network

“…Some have been previously referred to ( [1], [25], [31], [34]- [36], [47]) and others have not. Most of them are central tendency (mean, median) and dispersion (coefficient of variance) measures derived from computations performed on isolated bends.…”

“…These three conditions make the classification task easier. A principal component analysis (PCA) has been applied, as suggested by [25], [31], [33], or [47] over a set of measures, for choosing those measures which are more significant and not correlated. The selection of measures for PCA must be carried out anticipating their & A mean can be used as the central tendency measure, but it is interesting to note the remark of [38], who suggest the median instead of the mean.…”

“…[34]- [36], [38], [47] lb cv Coefficient of variance of the bend length and baseline length ratio (adimensional)…”