Visual Analytics: Ramya's POV

Hi everyone! For my final project, I plan to explore the 2016 National Park Dataset: https://www.kaggle.com/datasets/nationalparkservice/park-biodiversity/data Problem Description: Nonnative species have been identified as a major contributor to biodiversity decline because invasive species are highly adaptive to any environment and can easily outcompete natives. This loss of native species is a particular issue for U.S National Parks that were created to be untouched and protected. According to the National Park Service, they disturb ecological processes, harm ecosystem integrity, degrade natural resources, interfere with visitor experiences in parks, and exacerbate climate change and fragmentation from land use change (NPS, 2021). In order to understand these non-natives, it is important to understand their distribution, where they have been reducing biodiversity, and potential reasons to why. Problem Objectives: Visualize the largest National Parks in size and their locations in...

Hi everyone! This week we learned how we can utilize animation in our R visualizations.  I chose to visualize both the annual average temperatures in the Contiguous U.S and Florida to see how we compare to the mean temperature pattern.  The data source: https://github.com/washingtonpost/data-2C-beyond-the-limit-usa/tree/main    I find this animation interesting because it takes a normal line graph and visually moves each fluctuation in temperature allowing the viewer to see the quick increases in the late 2010s. I had to do a lot of trial-and- error to figure out how I could make both the U.S and Florida animations sync in one graphic, so we can visually see the differences in the temperature trends. This syncing lets the viewer easily see the more stable temperatures of Florida compared to the average U.S temperatures until1980. After 1980, Florida experienced major fluctuations and violet increases in temperature that is not seen as extremely in the mean U.S t...

Hi everyone! This week we learned about social network analysis and how we can use ggnet2 in R to accomplish this. I absolutely love the new Netflix show "Wednesday" and the original Addams Family movies, so I decided to analyze which characters in the original movie have the most interactions with the other characters.  My hypothesis:  Wednesday will have the most interactions since she is seen as the main character. The dataset site:  https://moviegalaxies.com/movies/view/26/the-addams-family/#  I imported the json file with the nodes and edges. I set the ggnet2 parameters to vary the size of the node based on the "indegree" or amount of interactions that character has overall. Result: From the visualization, we can see Granny has the most connections (9) and Wednesday has very few connections.  I enjoyed using ggnet2 to create this social network. The biggest challenge of creating this social network was finding a dataset for the visualization and customizing...