Towards Support of Visual Analytics for Synthetic Information
Agashe, Aditya Vidyanand
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This thesis describes a scalable system for visualizing and exploring global synthetic populations. The implementation described in this thesis addresses the following existing limitations of the Syn- thetic Information Viewer (SIV): (i) it adds ability to support synthetic populations for the entire globe by resolving data inconsistencies, (ii) introduces opportunities to explore and find patterns in the data, and (iii) allows the addition of new synthetic population centers with minimal effort. We propose the following extensions to the system: (i) Data Registry: an abstraction layer for handling heterogeneity of data across countries, and adding new population centers for visualizations, and (ii) Visual Query Interface: for exploring and analyzing patterns to gain insights. With these additions, our system is capable of visual exploration and querying of heterogeneous, temporal, spatial and social data for 14 countries with a total population of 830 million. Work in this thesis takes a step towards providing visual analytics capability for synthetic information. This system will assist urban planners, public health analysts, and, any individuals interested in socially-coupled systems, by empowering them to make informed decisions through exploration of synthetic information.
- Masters Theses