Data Visualization in Engineering Pedagogy through Determination of Colour Variance in Contaminated Grass Samples

Abstract

Big Data and Data Analytics have in recent times become important areas of focus in academia, in business and in society. This paper utilises experiments involving data visualisation of oil pollution studies and their effects on environment for enhanced learning in engineering education. Tracking and analysis of images and the use of accessible applications for the analysis of acquired data revealed the level of impact of the different types of oil pollution on grass vegetation. In accounting for these changes the primary RGB colours and corresponding values are used. The use of spectral analysis applications available in spectroscopy and comparison of results would in future prove useful in assessing some aspects of these changes in relation to wavelength and colours changes. The results of these studies would contribute in no small measure to the determination of best cleaning strategies for oil spills.