Publication Date


Document Type


First Advisor

Vaezi, Mahdi

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Mechanical Engineering


Having sustainable energy and health systems are the main factors in the vision plan of every country. Both of these systems are correlated with a variety of frameworks, including social, physical, technological, political, and economic factors. Therefore, different types of analytics methods can be implemented to develop the required assessments for those who make plans since understanding the effect of such factors individually, also their interactions, and the overall effect is crucial. With this regard, applications of data analysis and geospatial techniques in both energy, and health systems have recently gained attention. The proposed research here deployed advanced data analytics methods and Geographic Information Systems (GIS) to study energy and health systems, obtain understandings and make predictions, also analyze the corresponding data based on their spatial location and organizing multiple layers of information into visualizations. The proposed research is comprised of two sections. First, Geographic Information Systems and different data analytics methods were used to evaluate the potential of Municipal Solid Waste (MSW) as a renewable energy source in the state of Illinois. Our results demonstrated that Illinois is capable of producing 6,295,385.77 MWH annual energy using incineration technology from MSW. Also, using Anaerobic Digestion (AD) technology in MSW management would enable the state to be capable of producing more than 1,140,493,710,450.00 Litres biogas per year. Second, we expanded the application of data and geospatial analysis in the health system and deployed advanced data analytics methods, geographical information system (GIS), and predictive epidemiological models to analyze the anti-contagion policies implemented by the states across the country to slow the spread of COVID-19. Also, by implementing a meta-analysis in conjunction with multi-criteria decision-making methods, a Lung Cancer Risk Index (LCRI) was produced representing the probability of individuals getting lung cancer. The methods that have been developed for the extended applications of data and geospatial analysis in health can be used for various complex decision making and index generating purposes in engineering disciplinary such as additive manufacturing to evaluate the effect of process factors (e.g., injection, concentration, material characteristics, speed, temperature and so forth) individually and collectively to optimize the process and increase the performance.


96 pages




Northern Illinois University

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