India's Progression in Chess

India's Progression in Chess is an interactive 3D data visualization project created in Spline 3D. The project's core objective was to collect research data from sources including Wikipedia and Chess.com and transform it into a highly interactive and engaging spatial format. The visualization uses a highly interactive chess-themed interface to display India's medal history in global tournaments, underscoring the country's recent rise in international chess.

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Key Accomplishments

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Data Sourcing and Transformation:

Successfully executed the course objective by collecting and curating raw tournament data from Wikipedia and Chess.com. This data was then processed and mapped onto a 3D chessboard visualization to show Year-wise and Tournament-wise medal counts.

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Spatial Data Representation (Spline 3D):

Demonstrated proficiency in Spline 3D by developing a web-ready, interactive 3D environment. The quantitative data is effectively represented by the height of individual chessboard squares, which act as dynamic 3D bar charts.

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Intuitive Interaction Design:

Implemented dynamic user feedback crucial for engagement. Chessboard squares rise on mouse hover to display exact data values, and a point light follows the cursor to guide the user's focus and enhance the spatial quality of the visualization.

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Categorical Data Mapping:

Effectively visualized the multi-dimensional data by assigning the King, Queen, and Elephant pieces to represent categorical data (such as Gender-wise comparison and medal type), enabling rapid visual analysis of different achievement cohorts.

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Highlighting Progression Trends:

The visualization's structure was designed to underscore the significant upward trend in India's chess performance, with the height differences in the 3D visualization directly representing the steady annual increase in global medal counts.

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Challenges & Learnings

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The main challenge involved optimizing the complex 3D geometry and dynamic lighting within the Spline environment to ensure a smooth, low-latency experience across various web browsers. A key learning was determining the appropriate data scale to visually translate the Gender-wise Comparison and total medal counts onto the fixed grid of the chessboard without visually distorting the actual data proportions.

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