Homogeneity Of Positional Accuracy In Line Simplification Based On Douglas-peucker Algorithm

Many researchers of GIS do realize the effects that cartographic generalisation can have on data quality. Generalisation encompasses a variety of procedures in which source data are transformed to maintain cartographic fidelity during scale reduction, enhance computational efficiency, reduce data storage requirements, or merge data-bases with different resolution levels. Line simplification is only one component of line generalisation, which also encompasses operations such as smoothing, merging and displacement (McMaster1987). However, simplification is arguably the critical ine generalisation operation in transforming scale of spatial data.Researchers have studied spatial line quality and cartographic generalisation in isolation, but for the most part the interaction between these two domains has received little attention. The notion that generalisation causes a deterioration in line quality is not a new idea but there has been only a smattering of work in which uniformity of the errors introduced by generalisation are assessed quantitatively.This study examines the relation between generalisation and data quality for line simplification.Line simplification involves the selective elimination of vertices along a line. No new vertices are created. The simplified line has fewer vertices, and hence a simpler geometry, than the original line. The discrepancy between the geometric positions of the original and simplified lines is referred to here as positional distortion or positional error.There are many line simplification algorithms but the best-known and probably most widely-used is the Douglas-Peucker algorithm (Douglas and Peucker1973). The study focuses on the Douglas-Peucker line simplification algorithm and examines both natural and anthropogenic features (streams rails and roads)The study developed three objective methods that can be used to select an appropriate level of simplification, in order to facilitate compliance with well-distributed accuracy standards. The first methord is MVD-space filtering that describes maximum vector displacement qualitatively in every segment, managing the ambiguity of maximum vector displacement in an organized and natural way. The second methord based on standard deviation of maximum vector displacement in every segment. The third methord based on the range and maximum of maximum vector displacement in every segment. The study developed the law to show the mutation of uniformity of the errors introduced by simplification, and then try to analyze the reasons.This paper presents a solution to solve how to keep accuracy uniformity in simplified line. In this approach, the user supplies the Douglas-Peucker algorithm to simplify line for desired accuracy uniformity. Although the present research approached the problem at a preliminary stage, promising results have been produced. It is apparent that further research is required based on a more in depth analysis of uniformity of the errors introduced by simplification.