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, business, and society. The use of such tools allow for the in-depth analysis of more complex or nuanced issues that in turn allow the results to be examined and understood by a less specialised user base. In the case of environmental engineering there are a number of strategies for determining pollution levels of contaminated areas, however many of these rely on an existing knowledge of that field in order to be fully understood. By applying an analytical framework to the complex issue of environmental engineering, it is hoped that the results yielded could better serve the understanding of those outside this specialist industry. This paper utilizes 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 Red, Green, and Blue (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.