Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1393-1400, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
14 Sep 2017
T. Wu1,2, Y. Zhou2, and L. Zhang2 1School of Information Engineering, Lingnan Normal University, Guangdong Zhanjiang 524048, China
2Guangdong Engineering and Technological Development Center for E-learning, Guangdong Zhanjiang 524048, China
Keywords: Data Visualization, Spatiotemporal Data Analysis, Trajectory Data, Visual Analytics, Artistic Style Abstract. Rapid advance of location acquisition technologies boosts the generation of trajectory data, which track the traces of moving objects. A trajectory is typically represented by a sequence of timestamped geographical locations. Data visualization is an efficient means to represent distributions and structures of datasets and reveal hidden patterns in the data. In this paper, we explore a cloud model-based method for the generation of stylized renderings of trajectory data. The artistic visualizations of the proposed method do not have the goal to allow for data mining tasks or others but instead show the aesthetic effect of the traces of moving objects in a distorted manner. The techniques used to create the images of traces of moving objects include the uncertain line using extended cloud model, stroke-based rendering of geolocation in varying styles, and stylistic shading with aesthetic effects for print or electronic displays, as well as various parameters to be further personalized. The influence of different parameters on the aesthetic qualities of various painted images is investigated, including step size, types of strokes, colour modes, and quantitative comparisons using four aesthetic measures are also involved into the experiment. The experimental results suggest that the proposed method is with advantages of uncertainty, simplicity and effectiveness, and it would inspire professional graphic designers and amateur users who may be interested in playful and creative exploration of artistic visualization of trajectory data.
Conference paper (PDF, 2443 KB)

Citation: Wu, T., Zhou, Y., and Zhang, L.: ARTISTIC VISUALIZATION OF TRAJECTORY DATA USING CLOUD MODEL, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1393-1400,, 2017.

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