Vinsamlegast notið þetta auðkenni þegar þið vitnið til verksins eða tengið í það: http://hdl.handle.net/1946/15343
As human beings, we often wish to visualize certain information in order to make better sense of it. This can be a somewhat challenging enterprise for large amounts of data and might require downsampling the data, retaining only the important visual characteristics.
The focus of this thesis is to explore methods for downsampling data which can be visualized in the form of a line chart, for example, time series. Several algorithms are put forth in the thesis and their features are discussed. Also, an online survey was conducted where participants were asked to compare downsampled line charts against a non-downsampled chart. Some of the algorithms are based on a well-known technique in cartography which involves forming triangles between adjacent data points and using the area of the triangles to determine the perceptual importance of the individual points. According to the survey, algorithms based on a triangle area approach consistently proved to be effective, and one in particular when efficiency is also taken into account.