Media Arts and Technology

• 1.Pull checkout data
  2. Pull inventory data
  3. Merge checkout data and inventory data
  • This was the most challenging part as it required keeping a data column that would keep track of the inventory over time as it updates. So basically, every time the inventory changed, I’d have to have the new inventory reflect in the merged dataset. Additionally, since the inventory was missing some data, I had to impute the missing data. Attached is a screenshot of some of my Python code:

• 1. How the library responds to demand over time. It seems to generally lag, but they also generally responded to demand in some way. One interesting insight is that the library seemed to reduce the inventory of tablets due to lack of interest.
  2. Headphones and laptops are often checked out at high frequencies, whereas tablets and hot spots are hardly checked out at all.
  3. There is a huge number of hot spots available for checkout, but they are rarely checked out.
  4. You can see a predictable trough in each of these materials during COVID.

nefeli wrote: ↑

Thu Feb 15, 2024 2:50 pm

InterWeaving

This exploration seeks to understand the interplay between traditional art forms such as textile art and modern technological fields like AI, as evidenced by patterns observed in Seattle library checkouts.

After the sudden expansion of AI in the creative field, some artist expressed their concern about the traditional art forms. This exploration aims to understand how traditional art forms like textile art are related or influenced by modern technological fields like AI, as reflected in the Seattle library checkout patterns.

I explored different queries throughout the first weeks of the course and after careful consideration, I concluded that this query provides more suitable results.

Code: Select all

SELECT 
    title,
    itemtype,
    DATE(cout) AS CheckoutDate,
    DATE_FORMAT(cout, '%H:%i') AS CheckoutHour,
    TIMESTAMPDIFF(HOUR, cout, cin) AS CheckoutDurationHours,
    deweyClass
FROM
    spl_2016.inraw
WHERE
    (LOWER(title) LIKE '%artificial intelligence%'
        OR LOWER(title) LIKE '%machine learning%'
        OR deweyClass IN ('006.3'))
        AND cout >= '2021-01-01'
        AND cout < '2024-01-01'
ORDER BY cout;

After extracting the data, I began visualizing the data with Check-out Date in the X axis, Check-out Time in the Y axis and the Check-out Duration in the Z axis.

A first draft visualization looked like this:


After spending some time otimising the visualization the log() function helped into a better distribution of the points. The Curve line needs to be optimized to reach both the beginning and the ending point. And the color palette need to be refined to provide a better understanding between the two topics.

Final color palette:
The final results are these:

,
,


There are buttons to turn on/off the box and the grid. A button for each Category being visualized and a slider changing towards the Y axis to have a better view on the data.

• 1. Color of the inventory layer changed to gray
  2. Re-ordered layers
  3. Made grid lines less visible
  4. Changed label orientation
  5. Removed "Months" label on x-axis
  6. Mouse hover on the higher of the checkouts/inventory data layer at vertices will show number of checkouts and number of inventory at that month/year for that equipment
  7. Added graph title