Created: September 22nd, 2017
Outcomes and findings_
Our team was about noise, how noise can be track and how the data collected can give us a different understanding of our environment. We explore various locations throughout CMU campus and the adjacent neighborhoods of Shadyside, Oakland, and Squirrel Hill. We know that these places are where our colleagues live and work. The university their student's common spaces on campus where we are supposed to use it for studying in a quiet and chill matter. Nevertheless, these situation is not always ideal. So as a group we formulate the query, Is there a quiet place where a SoA student can study?
The last questions push us to think in various locations in and outside campus to start tracking noise levels. But, we believe that we don't represent a big group among the total number of students. So we create a survey to find out where our peers study. Collecting this information gave us the foundation to start collecting the data.
I found interesting that CMU students tend to stay on campus for their studying choices (see survey results). So we explored other areas where people can also use. Places in the University of Pittsburgh, around Shenley Plaza, and cafes where the places selected.
The outcome is that cafes and traditional libraries are no the ideal quiet place to study. These places get busy during the day, and people use them not only to sit and read but to chat, meet, even eat and sleep.
The less traditional places, like the Frick gallery, are the ideal places to go. There also a big difference between weekday and weekend time, so consider both results is also important.
1. Make the survey, you can see the result of the survey using this link: https://www.surveymonkey.com/results/SM-HQP882288/
2. Pick the places to go, collect the data using the same app - Decibel 10 (https://itunes.apple.com/us/app/decibel-10-noise-dba-meter-fft-spectrum-analyzer/id448155923?mt=8), pick a common place where the group can use it as a reference point, analyze and put all the info in a clean data set, you can see the data set using the following link: https://drive.google.com/open?id=13oMot1jOTSZjW2jCqbL4DYrucb-ytdBgapfMyJSyt-4
3. Pick relevant information and work in Carto to create a graphic outcome to share, you can check the data visualization using the following link: https://carnegiemellon.carto.com/u/paulmoscosoriofrio/builder/9ea4603f-7db9-4e61-a642-ddd3d5547386/embed?state=%7B%22map%22%3A%7B%22ne%22%3A%5B40.43257561752109%2C-79.9764347076416%5D%2C%22sw%22%3A%5B40.45929098138805%2C-79.89232063293458%5D%2C%22center%22%3A%5B40.44593462678334%2C-79.9343776702881%5D%2C%22zoom%22%3A15%7D%2C%22widgets%22%3A%7B%226815a011-975d-4639-9048-7c6779bf2ab8%22%3A%7B%22normalized%22%3Atrue%7D%7D%7D
4. Get a conclusion and a final report.
Note: point 3 is not completed yet because of technical problems using the software.
Personal reflection _
This exercise has helping me to understand the complexity of collecting, analyzing and presenting data. As a practical application, the idea to study a concrete case instead of something random has also proved the validity of data to present the real world using objective and quantifiable methods. As a learning experience, I value the possibility to interact with different types of software and explore the fundamental knowledge of the data collecting and visualization fields.