DATA STORES AND VISUALIZATION: Cookstove Emissions Research as a  Case Study for Industry Practice

Newgord, Alexia

Undergraduate Honors Thesis

DATA STORES AND VISUALIZATION: Cookstove Emissions Research as a Case Study for Industry Practice Public Deposited

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Citeable URL: https://scholar.colorado.edu/concern/undergraduate_honors_theses/0v838098q

Abstract

Research of Emissions, Air Quality, Climate, and Cooking Technologies in Northern Ghana (REACCTING) is a group composed primarily of researchers from the National Center for Atmospheric Research, the University of Colorado at Boulder, and the Nvrango Health Research Center. Recent reports indicate that “nearly three billion people in the developing world cook food and heat their homes with open fires or cookstoves that are fueled by solid biofuels.” The smoke exposure from these methods contributes heavily to global emissions and is estimated to cause four million premature deaths annually. REACCTING has been collecting vast amounts of data in search of effective alternatives for these activities and requested student assistance in managing and visualizing the data. The processing, cleaning, storage, and visualization of the REACCTING data presented technical and human challenges that are common in industry practices. This paper primarily discusses the technical strategies and design considerations for data visualization, within the context of the data engineering lifecycle. The process of working with the REACCTING data was dynamic and the end product was an explanatory web application that presents a dataset in the form of an interactive animated map. Although the REACCTING team was already using both free and proprietary high-level software for advanced statistical, geographical, and graphical analysis, this mapping “quick-look” tool served the explanatory purpose of creating an overview of the subject’s location and how that relates to exposure. The technical outcome can be used for a more comprehensive understanding of the data and, from a developer’s perspective, can be applied in concept to other data problems.

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