Final Project: Data Visualization (Ngelek, Dylan, Reed, Khizar)

What is the Data?

The data we used to construct our visualization comes from the Carleton Zoobooks and Social Explorer. The Carleton Zoobook is a collection of information about students including their original one-card pictures, high school names, and hometowns. To begin the cleaning process we began by copying and pasting the Zoobooks transcripts for the years of interest, 2015 – 2024, and then importing Excel commands to filter out all of the information except for the hometown location. Then using this data of hometown locations from different admissions class years we chose to overlay it with the data we found from Social Explorer which shows a poverty index for the country with specific information sectioned off by county as default but you can also change that given the other options presented such as state.

What tool or technique did you use to visualize it?

To create our visualization we used Social Explorer where we initially found the original poverty index data. The process of importing our Zoobook data was a little challenging, but in the end, we were able to import the data set and ensure the visualization looked how we intended it to.

What is the data visualization doing (exploratory or explanatory)?

We hope that this data visualization allows for both exploratory and explanatory viewing. Our visualization as you can probably see by this point aims to look at how admission classes change over time, and how the demographics of those admissions classes such as the poverty rate of admitted students’ hometowns change over time. This provides an exploratory viewing opportunity as it provides a holistic picture of how things change over time without trying to paint a specific picture. However, something in particular we wanted to look at was how the COVID-19 pandemic may have impacted these demographics and overall admissions class makeup. This part of our visualization can be seen by moving from Map 1 to Map 2 to see a pre and post-COVID-19 map. This function allows for a more explanatory way of looking at the visualization, as we can see how trends might change or stay the same given the pandemic. Something we noticed from this addition is that even given the pandemic Carleton’s admissions kept a very similar trend for where admitted students came from. This to me is pretty surprising given a large part of the Carleton population comes from out of state and therefore students during the pandemic who I would have thought might’ve been intimidated to go to school out of state during the time didn’t affect the hometowns of admitted students layout.

What you have done to style the visualization to increase its clarity?

To increase clarity as mentioned earlier we incorporated two different maps to differentiate the data by the pandemic’s starting point. Another thing we incorporated into this visualization is a legend along with different colored points for the different admissions class years. When it comes to the map itself without the Zoobook points we also ensured that the colors indicating different poverty levels based on the index were noticeably different so even though you could interact with the map to see these levels you could still just look at the map and start to develop some conclusions. Finally, you can sort through different means of looking at the poverty index, instead of differentiating by county, you could choose State, Census Tract, Census Block Group, Zip Code, Census Place, or MSA.