A week of Dawgs

ASSIGNMENT 2: Dear Data


About the Data:  I tracked the number of dogs I spotted for a week and it was pretty fascinating.  Collecting the data was exciting at first —  I spoke with the owners to learn more about  their dogs. Eventually, it became exhausting and I decided to take pictures and guess the breed or search online for a match using what-dog.net . What Dog classifies dog breeds using machine learning. Most of the photos I uploaded were  close-ups and the site returned  pretty accurate results for  each photo. I began tracking by using an application on my phone but there were too many steps involved and it was distracting so I decided to track with a notepad and pen. I recorded the breed type, color and size of each dog during my commute to and from Brooklyn and Manhattan each day/night. I transferred the data from my notepad to excel and  calculated the total count for the week along with the total count of different colors and the total count based on dog size. The data viz starts from the top of the postcard (day 1) and ends at day 5, the last day of tracking. I stayed in on Friday, Saturday and Sunday but managed to spot a dog while looking out of my living room window .  My excitement is clear and obvious in the first few days of data collecting; I started on the morning of Tuesday, Feb 5th and ran around the city like a mad person looking for data (dogs). By Thursday night, I only recorded dogs within walking distance or close enough to determine the breed. Here are the final numbers:

Total # of Dogs: 53

Tuesday: 15

Wednesday: 27

Thursday: 9

Friday: 1

Saturday: 1

Side note: I forgot to include my dog!!!, Magic Voilà Reggler (He’s the last dog on this page — sorry Magic!)

Any thoughts about how your self-tracking ideas might be refined as you gain more exposure to the discussions about self-tracking…

I learn something new with each self-tracking/tracking assignment, particularly getting into the habit of paying closer attention and  making a deliberate effort to change my perspective to see more around me.

Data

 

 

 

 

 

 

A1.3: Reflection

Week 1

I don’t have much of a relationship with self-tracking. I tend to stay away from tools and applications used for tracking activity. I guess I’m uncomfortable with the amount of information accessible and readily available online. My Facebook account has been inactive for almost 3 years and I try to limit the amount of information I share on other social media platforms. However, I’m not against the quantified self movement and encourage people to use the tools available to increase self-awareness and make constructive life adjustments.

I have a completely open mind for this course and I’m willing to test some self-tracking tools with limitations. I’m interested in emotion tracking, specifically how music impacts my mood and concentration. I’d like to know what music makes me happy/sad, how my mood evolves throughout the duration of a particular song, what part of the song triggers emotions, my productivity levels while listening to music, and how the data could be used for better decisions. At this point, I’m not entirely sure how I plan to track the variables of interest and I expect to face challenges with consistency.

Other topics to discover:

Pain management and food consumption: How my body reacts to certain foods . Why does my X hurt on X day? What foods trigger symptoms?

Sleep patterns and mood

Relationships – my connections with family and friends

I got a Fitbit as a birthday gift a few years ago and it collected dust after only a few weeks. The data was always inaccurate  and I was discouraged after a few bad readings. I’d like to take a different approach with activity tracking this time around and have patience for possible glitches and data inaccuracies.

Video Mapping — “All in my head”

I thought about how often I stare out of windows, namely from one of my windows at home that streams very little sunlight. I wouldn’t call this window completely useless (it’s across from another apartment building) because I often allow my mind to drift off into different spaces. Often times, I envision myself on a warm beach listening to waves form as they furiously crash against its shore. This recurring experience gave me an instant idea for my next assignment. In the interest of saving time, I purchased a wooden frame from Blick Art and used double-sided tape to create four identical boxes similar to a standard sized window.

I collected a few of my favorite tropical videos on YouTube and used Adobe Premier to edit the audio and video placement. I ran into issues rending a single video in four different partitions using MadMapper.

Attempting to troubleshoot this took a significant amount of time so I decided to remove the double-sided tape and go with the “picture style” window frame – a one pane glass effect.  

Mad mapping without frosted plexiglass

Mad mapping with frosted plexiglass

 

 

 

 

 

 

 

PlexiGlasssss

The video projected much cleaner and easier on the picture style frame during initial testing; mapping was also now less time-consuming and straightforward. I used plexiglass as the front glass but removed it because it affected the quality of the video. 

Challenges:

  • I’m an absolute novice with Adobe Premier and found myself spending a lot of time researching basic functions. In the end, it was extremely helpful and useful; however, I’d like to spend more time building on features and functionality. 
  • Fade out black. I want the end of the video to quickly fade out black with the audio still playing. 
  • Masking. I was able to mask out the logo on the left bottom of the video but this wasn’t useful once the frame changed and a new image appeared in the same video.

Concept:

I used Morning Mood by Edvard Grieg as background audio for “All in my head” because it evokes a feeling of peace and serenity, at least for me. In the end, the audio is abruptly interrupted at the climax of the piece by my own reality – the ever so impatient, NYC. Move or be moved, that’s the motto. 

Final iteration:

I’d like to expand this project into a social experiment by allowing the user to interact more with the video. I would place a timer on each location and monitor the length of time spent before a user disconnects from the image and manually changes the scene. In the end, I think it would be interesting to record how long it takes for one to become uninterested/impatient in the now or perhaps interested/anxious in what lies ahead. Is patience/impatience influenced by our current surroundings ? I don’t know, maybe its all in my head…