Dear data postcard

A2: Dear data postcard

Assignment prompt:
“Last week we scoured our tools and services for data that has and is being produced about us whether consciouly or not. This week, we make an effort to be deliberate about our tracking and to “practice paying attention… to strengthen the ability to see what is overlooked” and ultimately to fight what Rob Walker calls, “the war against seeing”; Walker is concerned that companies occupy every corner of our attention leading us to interpret the world on terms invented by the companies.

To do this you will, with a partner, embrace your inner Lupi-Posavek and “Dear Data” a feature of your life that might be illuminated through visualization.

About: Using “dear data” as a guide, create a set of rules to visualize your data. Think about the ways in which Georgia and Stefanie parameterized the aspects of their records to produce their visualizations. Find ways to express your own personal style and aesthetic in the implementation of your rules.”

The assignment is inspired by:

https://vimeo.com/179231295

 

I am tracking the amount of rats I see in the New York City Subway system while waiting for the train. I am looking at the tracks, because that is where I usually see them.

 

For instance: Monday

Station: 8st nyu – rw

Time: 21.38

Rats: 0

Waittime: 8

 

Station: times sq – 123

Time: 21.59

Rats: 0

Waittime: 4

 

Process

I wanted to visualize the data in terms of my expectation, so I chose the presence or absence of a big rat as the starting point.

Then I wanted to use the data to create a pixel mosaic in terms of when I took the train and how long I waited for it. I thought a lot about how to obscure that data or not, so that my address can’t be surmised.  I thought about whether or not to congregate the data (see first image or all of the counts). But I realized that if you where to, e.g draw a line showing wait times, one for each minute waited, in different colors – so 5 minutes * 2 times = 10 lines, that wouldn’t say much about when I waited. Also, since I saw the Rats at Broadway Lafayette and 103 st. both places where I waited more than 5 minutes, this data would be lost. So I decided to make the station name a shown parameter.

On a sidenote, I discovered that I rarely wait very long for my trains (though I always felt like I do).

It is also very apparent that I have no set schedule, since my traveling times fall all over the 24hr day.

 

I think this data postcard would have looked a lot more interesting if I’d taken the train more this week.

Now looking at it, I think daring lines either inside or outside of the rat shape would have been more interesting, instead of making the color more opaque.

There is one thing that is apparent from my visualization though: longer waiting times = more possibility of spotting a rat.

 

  

 

Full data set:

Rats when waiting for subway NYC

Monday

Station: 8 nyu

Time: 21.38

Rats: 0

Waittime: 8

 

Station: times sq 123

Time: 21.59

Rats: 0

Waittime: 4

 

Tuesday

Station: 72 bc 

Time: 9.38

Rats: –

Waittime: 0

 

Station: 23 st 1 train

Time: 16.44

Rats: 0

Waittime: 0

 

Wed

 

Thurs

Station: 72 bc

Time: 13.03

Rats: 0

Waittime: 2

 

Station: West 8 ac

Time: 20.38

Rats: ——

Waittime: 2

 

Fri

Station: 72 23

Time: 9.44

Rats: 0

Waittime: 0

 

Station: 42 times n

Time: 09.54

Rats: 0

Waittime: 0

 

Station: delancey f

Time: 20.19

Rats: 0

Waittime: 5

 

Station: Broadway lafayette b

Time: 20.27

Rats: 1

Waittime: 5

 

Sat

72 23

10.49

0

1

 

Times sq

10.57

0

3

 

8th street n

15.33

0

1

 

Times sq 23

15.46

0

0

 

72 1

00.30

0

2

 

103 1

03.23

1

6

 

Sun