Data Collection
To begin the final project, we were asked to collect three sets of data over a consistent period of time, one being related to weather. Because I was interested in how the weather would have an impact on my daily productivity, I chose to keep track of my phone usage as a criteria for productivity. I’ve noticed that I am more inclined to work or be outside for longer periods of time when it is warmer outside. I also have a preference of sitting in very sunny places when I work, so I was interested in seeing whether the different datas would create a pattern.
In addition, I also wanted to track my activity levels based on steps taken throughout the day to find out whether I go outside and walk more when it is hotter outside.
Based on these initial predictions, here are my question and editorial assertion:
How does the weather affect my productivity based on the amount of time spent on the phone?
Time spent on the phone increases when the weather is colder, leading to a decrease in productivity.
The exact datas I will be exploring are:
Weather- record the range of temperature throughout four time periods of the day. I used this website to record precise changes in temperature every day: https://www.timeanddate.com/weather/usa/pittsburgh/historic
Phone Usage- Number of minutes spent on social media four time periods of the day. I used the screen time feature on my phone and eliminated time spent listening to music or other functions since that may overlap with productivity.
Activity Level- Number of steps taken four time periods of the day. I used the health app to record this data.
In our Data Visualization Abstract, we were asked to find two examples of visualization. I chose visualizations where there was a clear comparison between datas and used bright colors to inform clarity. These examples, especially the one that compares two populations, ended up inspiring a lot of the design decisions I made for the final iteration.
Dear Data
For this assignment, I exchanged postcards with my partner Dreami in our data visualization. I first decided to play around with two sets of data in order to maintain some clarity. Because all sets of datas have the common denominator of being recorded throughout hours of the day and is split into 12am, 6am, 12pm and 6pm, I thought it would be interesting to overlap the two information on one timeline. On the left side, I used horizontal bar charts to display the overall phone usage and average temperature each day for a more simple comparison. Some helpful feedback I got from class and my partner were:
- because the type of data for weather and phone usage are measured in different units (F and minutes), it would be confusing to represent them in the same way on the same time line.
- For the bar charts, the feedback I got was that since one of the bar represents an average number and the other represents total, the datas are not best compared in this way.
- Overall, the overlapping is an effective visualization method
As this is the first time I’ve put some of the data together, I was able to observe some patterns. For example, I would always have a small peak in phone usage around 12am, which is often unrelated to weather and more about habit. My phone usage would gradually increase throughout this week, while the temperature consistently decreased throughout the week as well, which fits with my editorial assertion so far.
Physical Sketches
With the feedback from dear data, I made further visualizations using some physical objects and sketching. As I did not have access to many materials, I decided to look for unique things available to me that I could challenge myself in creating visualizations.
I began with chips and salsa, and wanted to play with scale and overlap. The size of the chips represent the rise in temperature levels, while the amount of salsa on the chip represents increase in phone usage. Although this method was not the most effective for my data specifically, I thought it was an interesting attempt. There is a relationship shown when the size of the chip and the amount of salsa balance out and cover each other.
I then experimented with directionality and quantity, visualizing temperature and phone usage in a week. The direction of the chili represented the degree of temperature of the day in the first one. In the second one, the size of chili represents temperature and each blueberry represents an hour of phone usage.
For the next set, I wanted to try a more standard sketching of the data comparison. Since on my phone, both the data recordings of steps taken and social media usage are represented in bar graphs, I wanted to compare them on the same graph to see if there are any patterns. On the left, I sketched out a day of activities and on the right I sketched out a week of data. Similar to previous feedback, I still needed to work on differentiating different sets of data when they are not measured with the same units and having only certain sets of conditions to define them (ex. color for temperature, scale for phone usage).
I wanted to represent the temperatures in a more visually captivating way while highlighting the changes throughout the day. I also wanted to use overlap in the data since one common unit they are measured with is the four periods of time in a day.
Moving towards digitizing the data visualization, I made a more refined version on my ipad that includes a ring representing the circular timeline of the day. The hatching, representing phone usage, overlaps on top of color, representing temperature change. Each ring represents 30 minutes and the ring begins at 12am, like a 24hr clock. I really liked how the hatching and color are able to create a relationship and comparison without one covering another.
I further worked on visualizing the data for the whole month of April. It was surprising to me the range of temperature that occured throught the month, even just in one week or day. It is also evident that during really cold days, phone usage would increase dramatically. This is really interesting to see because I had always assumed that the weather affected my attention span in working and productivity, but never saw the influence directly laid out through a visual pattern.
In addition to the two sets of datas, I added a third set of data that tracks my steps throughout the day. This is visualized using scale and on the outer ring in order to not interfere with the other information.
The steps taken is colored white and represents a more positive growth on the circles as they are meant to look like they’re blooming and flourishing the more steps I take. The black hatching, on the other hand, is meant to look like it is contaminating the day and time as it dulls the colors of the ring at overlapping spaces.
It is interesting to see that at certain points, the curving outline of the hatching would follow the shapes of the white circles on the outside, such as April 14th and April 25th.
In this version, I wanted to see if I could compare the two extremes of April side by side to further prove my editorial assertion that warmer weather leads to less phone usage and more activity. I took out the hottest and coldest days and enlarged them with more information. There is a stark contrast in both activity levels and phone usage.
In my final version, I took in the feedback and decided to remove the scaled-up details of the hottest and coldest days and instead marked them within the overall calendar in order to increase the size of my key, which was relatively small in the previous version.
Overall, I learned a lot in this visualization project, not only about effective ways to display and compare datas but also about my personal habits.
