The graphs shown above are a quantitative data visualization of the change over time for Bernardo’s, my pet fish, water parameters. As Bernardo’s main caretaker, I change his water at least once a week. While performing this water change, I also perform basic chemical tests to ensure that the water he’s swimming in is the healthiest it can be. The four key water parameters that I tested for were ph, ammonia, nitrates, and nitrites. My data sample size consists of the measure of these four parameters over nineteen weeks.
To create this set of charts, I input all the data collected into a spreadsheet. The date as well as each parameter I was tracking received their column. After entering all of the data, I created a column graph for each of the individual parameters, as well as one that combined all of them. The role of the graphs that contain all of the parameters together is to show overall change over time. This information could be useful to determine the overall effect the combinations of changes in the parameter had on the water as a whole. The individual parameter graphs serve to show changes on a micro level and isolate parameters that might not be following the same trend of change over time as the other ones.
I do not imagine that the specific set of graphs shown above would serve well for commercial use, I do think they could provide for excellent personal use. An example being if I were to travel or fall ill and need someone to take over the care for Bernardo. That said, although the graphs might only serve a personal use, the process of creating them would have more far-reaching applications. The data needed to create this kind of visualization would be numerical data that is collected on a repetitive/cyclical basis. A possible example of this would be a writing instructor that sends out a survey to all her students in which she asks them to rate key aspects of the course using a Likert scale. As more data is gathered over time, she would be able to utilize this visualization model to find positive or negative change spikes, allowing her to note what actions might have caused that change over time.
All of that said, there are dangers in using this type of visualization. The first one being that rigid “boxes” are needed to collect this type of data, which in turn eliminates space for the individuals providing the data to exercise their agency, thus possibly silencing them from articulating important information. It’s my recommendation that quantitative data collection and visualization be paired with qualitative data collection and visualization to open more space for these silences to be interrupted.