Visual Analytics: Ramya's POV

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 ge...

Hi everyone! This week we learned about basic visualization components including points, lines (communicate direction), circles, text, coordinates, patterns, colors and chart formats such as bar charts and pie charts. There is an apparent issue with visual perception of figures versus backgrounds.  I learned how to analyze geographic information through spatial patterns in maps, trends in graphs, and sequences in data using google dataset search in Tableau.  I chose to explore the Estimated Use of Water in the United States County-Level Data for 2015 dataset from USGS.  I modified this large dataset to only include the counties in Florida, the total groundwater withdrawal variable, and the total population variable for ease of use in Tableau. Using this data, I wanted to learn which states had the most groundwater withdrawal for saline and freshwater indicative of the county's water use.  From the above map, we can see an apparent trend of south eastern Florida count...

Hi everyone! My name is Ramya and I am excited to explore visual analytics within the scope of various programming languages to share the tips and tricks I learn.  I particularly enjoy map visualizations which is why my career aspiration is to be an environmental GIS specialist. One visualization that stood out to me recently was a very recent weather map displaying the flooding (mm) in Tamilnadu, India. I visited these areas with high rainfall and flooding during winter break. The bright colors and ease of understanding caught my eye in this heat map. Keim et al (2018) explain the significance of information overload in negatively impacting user understanding of information. Visualization is a great tool to communicate more information through simple diagrams than words alone can achieve. According to Keim et al (2018), visual analytics is a tool for decision-making beyond information visualization and this is seen in the flooding heat map I was intrigued by above. Throu...