Search found 5 matches
- Mon Mar 16, 2015 12:53 pm
- Forum: Winter 2015
- Topic: Proj 5: Data Correlation / Final Project
- Replies: 17
- Views: 39424
Re: Proj 5: Data Correlation / Final Project
Volumetric Visualization with Point Cloud This project is based on the previous assignment. In the volume, the X-Y plane represents all books in the library. Z represents time, from 2006 to 2014 monthly. Color at x,y,z means the "checkout density" at book location x,y and time z. The mapping from b...
- Wed Feb 18, 2015 10:57 am
- Forum: Winter 2015
- Topic: Proj 4: 3D Volumetric, Spacial Visualization
- Replies: 17
- Views: 16441
Re: Proj 4: 3D Volumetric, Spacial Visualization
Spatial-temporal Checkouts screenshot.png In this assignment, I built a visualization to show checkout trends of different kinds of books. The design is a volumetric visualization, using the X-Y plane to layout the books, and the Z axis to show the checkout trend. Book layout: The books are layout ...
- Mon Feb 02, 2015 9:29 pm
- Forum: Winter 2015
- Topic: Proj 3: 2D Reorderable Matrix
- Replies: 17
- Views: 15705
Re: Proj 3: 2D Reorderable Matrix
Self-Organizing Map of Checkout Trends screenshot.png This project uses Self-Organizing Map and Restricted Boltzmann Machine to visualize temporal checkout trends of each 2nd-level Dewey class in the Seattle Library Dataset. Explanation The query extracts the number of checkouts for each Dewey cate...
- Sat Jan 17, 2015 9:33 am
- Forum: Winter 2015
- Topic: Proj 2: 2D Matrix
- Replies: 18
- Views: 21895
Re: Proj 2: 2D Matrix
Average Lending Time for each Dewey Class and Year HW2-1.png HW2-2.png HW2-3.png HW2-4.png Description: The visualization shows the average lending time for each Dewey class and each year from 2006 to 2013, using a heatmap metaphor. The color scales are designed using HCL Picker (http://tristen.ca/...
- Wed Jan 14, 2015 8:51 pm
- Forum: Winter 2015
- Topic: Proj 1: Data Query
- Replies: 19
- Views: 21186
Re: Proj 1: Data Query
Average Lending Time One interesting question about this dataset is to see the difference between the check-out and check-in time (lending time), which can reveal how long people read the books to some extent. Although people may not read the book during the entire lending time, but this number is ...