Module 3: Vector Graphics

Hi everyone!

This week we learned about colors and how they are used for visualizing data. Utilizing Adobe Illustrator, we were tasked to visually improve our maps from last week. 

I had a lot of fun learning the ins and outs of Tableau last week, so I decided to create one more visual in Tableau: a bubble graph showcasing the per person withdrawal of water. This bubble graph indicates the larger sized bubbles have a larger total population, while the darker blue indicates higher per person use by each county (groundwater withdrawal / total pop). From this graph, we can see Walton County which is very small in population has the largest per person use, while Bay County which is relatively small has the lowest per person use. Interestingly, Miami-Dade is the largest county in size and has a relatively high per person use unlike the large county of Hillsborough with relatively low per person withdrawal. 

Through Adobe Illustrator, I added in a water aesthetic title and two AI generated images from the new tool in Illustrator for groundwater withdrawal on the right and per person water use on the left. I also added hand-drawn waves at the bottom utilizing the pencil tool. I believe Illustrator adds to the aesthetic of the map and gives space for extra image communication of the concepts being relayed in the map.

-Ramya's POV

Comments

Post a Comment

Popular posts from this blog

Module 8: Correlation and ggplot2

Image
Hi everyone! This week we learned about correlation analysis and how to utilize the ggplot2 package to visualize correlation.  A great method to visualize correlation or relationships between variables is a scatterplot with a line of best fit. Within the mtcars dataset, I wanted to gain a better understanding of how each variable correlated against each other to find a relationship between variables I find the most interesting to visualize. I used the corrplot package to generate this initial visualization. From this plot, we can see there is a strong negative correlation (dark red) around 0.7 for horsepower (hp) and acceleration (qsec). I thought this was unusual because a car with higher horsepower would make sense to accelerate faster since it generates more power. To explore this further, I created a scatterplot with a line of best fit. Based on this scatterplot, it is clear that increasing horsepower relates to a lower acceleration by the car. However, this graphic explains th...
Read more

Final Project: Biodiversity in U.S National Parks

Image
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...
Read more

Module 12: Social Network Analysis

Image
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...
Read more