Lab 4

For the final project, I am interested in creating a distribution map that shows the major religions throughout the country. I would make a point distribution to see where religion is clustered throughout the country and see obscurities like religion is low population areas. I am interested to see this because it could tell you a lot about culture of a certain area. You could also tell a lot about the types of people that live in certain areas. I would have to research places of worship in large cities through America. For example I would research the amount of Jewish temples in California place a dot at each one. If I do this throughout the country, it could tell an interesting story about religion in the United States.

Lab 2

For lab 2, I choose to take a look at “The Delek Achives”. This project is a research initiative by the Delek Education Foundation that intends to identity and shine light on religion-based discrimination in India’s schools. This project is different from most by the way it uses real first hand accounts of discrimination rather than data from surveys. Most stories from the project are written by people who are directly associated with the education system in India. The posts are usually concerned with deeply traumatic experiences that come from teachers, students, administration, and even parents. The hope of the Delek Archives is to reimagine the act of archiving as an open collaboration. The project aims to demonstrate that discrimination is just as much a personal narrative as it is a systemic pattern.

I like the approach this project takes. It uses a story to shine light on discrimination rather than relying on data to tell how horrible something like discrimination is. I think people respond much stronger to a personal anecdote than to data. To me, this project is successful and more projects dealing with emotional matter should consider a personal narrative approach.

Lab 1

I choose to highlight this exercise because it demonstrates how important time when dealing with data. When time is not important, the spiral is very loose and with a lot of space in between. When time is the most important component, the closeness becomes very precise and crucial.

For the schema, I would look at how much time was spent actively driving vs how much time the car was parked. This would be fairly straight forward as I would go down the list of times and note when the car was a rest stop, getting food, getting gas, etc. I would then total that time and the time spent driving which is indicated by the long stents in between stops. For the data diaries, I will be doing “How many variations to a line”, “Draw your breathe”, and “Color palette testing”.