Generating Progressively Vector Data Streaming for Adaptively Mobile Visualization

This paper proposes and implements a new solution for generating progressive vector lines and polygons data streaming for adaptive visualizations on small mobile devices. The proposed solution firstly recognizes the spatial patterns of vector data via a pattern recognition method, then generates a coarser Level_of_Detail of vector data with the preservation of main characteristics of vector data. Secondly, the solution generates continuous vector data through constrained remove operations of vertices whilst maintaining consistent topology via a set of constraint rules. Finally, the solution generates a compressed vector data streaming via a clustering method. The compressed vector data streaming is decoded for progressive visualization on small mobile devices. Experiments show good qualities of the resulting visualizations. The solution is shown to be scalable to large datasets, to produce graphically acceptable results and to automatically set a viable resolution according to scales.