Generate a spatially continuous synthetic population description for improved prediction of human movement and spread of disease

R language came into my life on the first day of college last semester. In my COLL 100 Data Science Lab, I used R Studio to sort through data, make predictions about stocks of Fortune 500 companies(stocks_map), create wordclouds from twitter data(wordcloud_modern_family), project crop data onto the map of Nigeria(dominant_crop_map_lga), etc. I was amazed at how powerful R is while dealing with big data and getting conclusions out of it. As a teaching assistant in the lab this semester, I am taking Human Development seminar and diving more deeply into data science. I learned about freedom and complexity, scale of the world, and cutting-edge methodologies involved in data science. The most engaging part is putting what is discussed in the research paper into reality with R. We made three-dimensional plots of WorldPop data of Liberia and other countries. While writing annotated bibliography, literature review, and central research question through the semester, I get more and more interested in the research and feel motivated to actually deal with the gap found from the previous papers in my own research. At the meanwhile, I am also taking classes from Computer Science and Mathematics department. With all of these inspirations from classes and encouragement from Professor Tyler Frazier, I came up with the idea for the research assignment in class — “Generate a spatially continuous synthetic population description for improved prediction of human movement and spread of disease” and plan to actually do this during the summer.

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