State elections results in India yield variegated and unique set of insights. Often it is hard to grasp the nuances of local electoral dynamics, and harder to communicate it to an audience not deeply engaged in the local political process, or to an audience from outside the state. While national news outlets do a fairly good job of analyzing and communicating the federal elections, the state elections in contrast have largely remained devoid of rigorous analysis and insightful communication. In this paper, using the data from the Assembly elections that took place in the state of Tamil Nadu in May 2016, we present a process and a set of interaction design and visualization methods to present complex insights. The general principles thus derived, will not only aid analysts and journalists to present their insights more effectively, but also empower the readers, depending on their level of interest and civic engagement, to go beyond what is presented and to discover new insights for themselves.

Visualization literacy of the broader audiences is becoming an important topic, as we are increasingly surrounded by misleading, erroneous, or confusing visualizations. How can we educate the general masses about data visualization We propose a twofold model for designing educational games in visualization based on the concept of constructivism and learning-by-playing. We base our approach on the idea of deconstruction and construction, borrowed from the domain of mathematics. We describe the conceptual development and design of our model through two games. First, we present a deconstruction-based game that requires the inspection, identification, and categorization of the visualization characteristics (data, users, tasks, visual variables, visualization vocabulary), starting from a finalized visualization. Second, we propose a construction-based game where it is possible to compose visualizations bottom-up from individual visual characteristics. The two games use the same deck of cards with a simple design based on visualization taxonomies, popular in visualization teaching.

The feasibility of machine learning and data mining has been recognized by past research in the analysis of biomedical device data. The proposed work analyses the biomedical Electroencephalography (EEG) device data using visualization techniques and these techniques have potential for extracting information from huge data sets. The presented work includes extraction of visual patterns using different visualization techniques. The pattern identification task is performed in several stages like outlier removal from EEG. After outlier detection data is divided into manageable segments to avoid overcrowding of data points. Then random method is applied to select a segment and identify prominent patterns by application of visual mapping techniques.