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The application is primarily designed in R to exploit the statistical and visual capabilities provided by the tool to design elegant graphics which provide the end user meaningful insights intuitively. Our application utilized geo_facets, parallel coordinates, treemap, chloropleths, bar charts, line plots, and heatmaps to display different ways of interpreting rainfall as well as crop data with varying levels of granularity. | The application is primarily designed in R to exploit the statistical and visual capabilities provided by the tool to design elegant graphics which provide the end user meaningful insights intuitively. Our application utilized geo_facets, parallel coordinates, treemap, chloropleths, bar charts, line plots, and heatmaps to display different ways of interpreting rainfall as well as crop data with varying levels of granularity. | ||
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Revision as of 13:55, 14 August 2018
Water For Life: India's Rainfall & Crop Analysis Through Visualizations
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Abstract
With growing fluctuations in climate in recent years, one occupation that has been affected the most is Agriculture. This has turned into a global concern and particularly in India as Agriculture is the backbone of Indian economy providing employment to a whopping 48.9% of country’s entire workforce while constituting 17-18% of India’s GDP. Agriculture in India is highly dependent on the spatial and temporal distribution of Monsoon rainfall. Over 80% of the annual rainfall is received in the four rainy months of June to September. This research and shiny application built in R attempts to uncover India’s Rainfall-Crop relationships using historical data ranging over years from 2000 to 2014. Our primary objective is to analyze the effect of changing rainfall on the overall crop productivity through various visualization techniques enabling users and analysts alike to explore the data visually and interactively. The application is primarily designed in R to exploit the statistical and visual capabilities provided by the tool to design elegant graphics which provide the end user meaningful insights intuitively. Our application utilized geo_facets, parallel coordinates, treemap, chloropleths, bar charts, line plots, and heatmaps to display different ways of interpreting rainfall as well as crop data with varying levels of granularity.
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Meet Our Team
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