Data Visualization for Reservoir Concentration
Semester: Spring 2025
Praxis Course: DSCI 310: Data in Action
Faculty Advisor: Jennifer Spohrer
Field Site: Discovery Center
Field Supervisor: Bria Wimberly
Praxis Poster:
DSCI_WaterProject_Revised_PraxisPoster-compressed
Further Context:
This semester, our team collaborated with The Discovery Center to create a visualization model demonstrating the evolution of abiotic factors over time, using data collected from Lake Vickers on the Bryn Mawr campus, provided by Professor Tom Mozdzer. The reservoir at The Discovery Center spans 38 acres and is just under 8 feet deep. Structurally, it resembles a bathtub, with steep walls leading down to a flat bottom composed of concrete and brick. Originally, water was pumped in from the Schuylkill River, but it is now primarily replenished through precipitation. It is currently the largest body of freshwater in Philadelphia.
By collecting and publishing data on the water chemistry of the reservoir, the community can learn more about the biodiversity and in turn improve the environmental conditions of the center. This will also make Professor Mozdzer’s data available for the college to further research on the sustainability of the campus. To make this work, we reproduced the graphs from Zentra Cloud in R, and created a StoryMap using KnightLab to make the data accessible and informational to the public.
We were able to use the data continuously uploaded in real time to the ZentraCloud platform through the sensors installed in Lake Vickers by Tom Mozdzer. Months worth of information on the environment and chemistry of the water stored on Zentra proved to be an invaluable source we could work with to create our model. However, Zentra is only accessible through obtaining the credentials of an account, which is expensive and in turn creates difficulties for allowing more people to use the data.
We took on the added challenge of recreating a graph from Zentra using R Studio, which is public and free, that would serve as a model for making this data accessible to Bryn Mawr. This would also allow for the data to be used in other classes and further the research started by Professor Mozdzer. We were able to write out the code for plotting graphs using csv files, which would be the outline for the Discovery Center to use once they collect their own data from the reservoir using sensors they intend on installing. This process pushed our coding skills and our ability to make use of the resources provided by the college, such as the office hours held by the Digital Humanities department.
In order to display the graphs created with R Studio, we searched for a platform that allowed The Discovery Center to present monthly water chemistry data from different locations in the reservoir and ultimately embed it onto their website. We found StoryMap, a user-friendly tool from KnightLab that allows users to add descriptions and graphs to various locations using coordinates which are important to graph points in the reservoir. This would be a great way to incorporate advanced graphs using R to customize visualizations. While StoryMap focuses on spatial data, StoryLine is another tool used to display information uploaded from spreadsheets to create interactive graphs. This tool also has the option of including descriptions for specific data points, allowing the public to understand changes in levels of dissolved oxygen, pH and temperatures over time, as well as other chemistry data.
Our next steps for the Discovery Center is to present the code we have been working on and our data visualizations to the staff in a virtual presentation in May. We hope for them to use our visualization model as an example and apply it to their own water data. In the long road, they would produce code that updates their visualizations regularly with real time data and embed the StoryMap onto their website. This would allow for communities and the public to view their water data and gain insights on water chemistry like temperature, DO, pH, etc at the center, while also including biotic factors as well like aquatic insects. This will help their goals in measuring how those populations vary over time in different locations, seeing what is affecting them.