Today we covered a lot of different topics, and so far this was my most interactive of the week. We went over some of our previous share LaTex homework since some of the students were having issues with citations and creating references using .bib files. I initially had problems citing a book but after sleeping on it I was able to notice an obvious syntax error!. We also worked on creating videos for future or ongoing projects as well as how to create a press release and used several examples from Washington state and Donald Williamson from IU who was awarded an NSF grant.  We also spent a lot of time working on Arduino Lilypad, which is essentially a programmable circuit board where you can create various outputs and inputs with coding.

Learning how to code the Arduino was quite difficult initially but I had very limited experience learning loops and conditional statements which really helped to get my code functional.

We also got to work on Tableau! finally something I felt I really had a grasp on. Over the course of the previous semester(about a week ago), I was studying R programming language, Alteryx (GUI running r-script)  and Tableau. I was really glad that a friend forced me to take this class with him because it made me feel finally confident about having that skillset today. Here are some of my visualizations of Garmin wearable data! Here I ran a data visualization of the sum(heart rate) column from a spreadsheet resulting from Garmin data from a graduate student.

Lastly, I read an article by Schatz, J., & Kucharik, C. J. (2014). Seasonality of the urban heat island effect in Madison, Wisconsin. Journal of Applied Meteorology and Climatology, 53(10), 2371-2386. In this paper, the researchers proposed a way to gather spatial and temporal variations in Madison, wi which has a population size of over 400,000. They were attempting to observe the urban heat island (UHI) which is essentially when an urban area shows a significant change in temperature in the surrounding rural areas. To measure this they used  150 sensors in agricultural locations, rivers, and forests.  They used regression models to analyze changes over time during the measurements of temperature change in March 2012 through October 2013 and used 11-day moving averages of temperature change. After 18 months they concluded that areas with environmental density or IMP were a continuous factor which allowed them to extrapolate to a large environment to map spatial and temporal variation.